tag:blogger.com,1999:blog-20546844381360456522024-03-14T02:57:23.860-07:00The Smart GridTowards a self-organizing energy infrastructureWilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.comBlogger102125tag:blogger.com,1999:blog-2054684438136045652.post-90968430187230263272023-12-06T02:52:00.000-08:002023-12-06T02:52:00.137-08:00On the Potential of Self-Organizing Energy Systems<p><!--[if gte mso 9]><xml>
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</p><p class="MsoNormal" style="text-align: justify;"><span lang="EN-GB" style="mso-ansi-language: EN-GB;"></span></p><div class="separator" style="clear: both; text-align: center;"><span lang="EN-GB" style="mso-ansi-language: EN-GB;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSoR8hCTl2x61uPsYQWBU7gOnPuXGPsm5D0xwXgN1Z56m2z_N6ILPcL5f9MiCDCMOMfWIV88QsooTMxzrkTcLmDJwGnvgX516S_imKK1o5q_r5UlB-EagcaCCaNYUX3111muxt_O2tepjvcBaRZs0zJw7uE8MencW8sh932EDWjaILntQwKR6YbckbnzI9/s1080/ACSOS_2023_Wogatai.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="757" data-original-width="1080" height="224" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSoR8hCTl2x61uPsYQWBU7gOnPuXGPsm5D0xwXgN1Z56m2z_N6ILPcL5f9MiCDCMOMfWIV88QsooTMxzrkTcLmDJwGnvgX516S_imKK1o5q_r5UlB-EagcaCCaNYUX3111muxt_O2tepjvcBaRZs0zJw7uE8MencW8sh932EDWjaILntQwKR6YbckbnzI9/s320/ACSOS_2023_Wogatai.jpg" width="320" /></a></span></div><span lang="EN-GB" style="mso-ansi-language: EN-GB;">In the rapidly evolving field of energy
management and autonomous systems, Kristina Wogatai presented her planned
dissertation, titled "Exploring the Potential of Self-Organizing Applications
in Energy Networks" at the Doctoral Symposium of the 4th IEEE
International Conference on Autonomic Computing and Self-Organizing Systems
(ACSOS 2023). Held from September 25th to 29th in Toronto, Canada, this
conference serves as a significant forum for sharing the latest research in
autonomous computing, self-adaptation, and self-organization.</span><p></p>
<p class="MsoNormal" style="text-align: justify;"><span lang="EN-GB" style="mso-ansi-language: EN-GB;">Modern society's increasing demands for
efficient and sustainable energy management make stable energy supply networks
indispensable. However, achieving this stability is challenging due to dynamic
environments and diverse constraints from various energy sources. Kristina's
research focuses on self-organizing applications as a potential solution to
these challenges. These applications enable network components to communicate
and collaborate without centralized control, making adaptive decisions to
respond to changing conditions.</span></p>
<p class="MsoNormal" style="text-align: justify;"><span lang="EN-GB" style="mso-ansi-language: EN-GB;">Inspired by slime molds, Kristina explores
their efficient pathways and growth optimization to balance energy demand and
load across network components and areas. Her work also addresses the concept
of resilience by developing fault-tolerant architectures for energy systems.
These architectures incorporate redundant components, alternative pathways, and
self-healing mechanisms for network stability, even in the presence of faults
or failures.</span></p>
<p class="MsoNormal" style="text-align: justify;"><span lang="EN-GB" style="mso-ansi-language: EN-GB;">Additionally, the study explores the
integration of nature-inspired approaches with advanced technologies like
artificial intelligence to enhance energy grid management. Overall, by focusing
on specific research questions and considering the combination of
nature-inspired approaches, advanced technologies, and energy grid
optimization, this research aims to contribute novel findings and expand the
existing body of knowledge in the field of self-organizing applications in
energy networks.<br /></span></p>
<p class="MsoNormal" style="text-align: justify;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-GB" style="mso-ansi-language: EN-GB;">Paper</span></b></p>
<p class="MsoNormal" style="text-align: justify;"><span lang="EN-GB" style="background: white; mso-ansi-language: EN-GB; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">Kristina Wogatai.</span><span lang="EN-GB" style="mso-ansi-language: EN-GB; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> <span style="background: white;"><a href="https://mobile.aau.at/publications/wogatai-2023-A_Graph_Based_Approach_for_Applying_Biologically_Inspired_Slime_Mold_Algorithms.pdf">Exploring the Potential ofSelf-Organizing Applications in Energy Networks.</a> In Proc. IEEE International
Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), </span></span><span lang="EN-GB" style="border: 1pt windowtext; mso-ansi-language: EN-GB; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-border-alt: none windowtext 0cm; mso-fareast-language: DE-AT; padding: 0cm;">Toronto, Canada, </span><span lang="EN-GB" style="mso-ansi-language: EN-GB; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">September 25-29, 2023.</span></p>
Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-44058110278595443172023-12-01T03:48:00.000-08:002023-12-01T03:50:09.181-08:00Energy Disaggregation with NILM on a Raspberry Pi with Smart-Metering Extension<p>Our recent work on Energy Disaggregation with Non-Intrusive Load Monitoring (NILM) on a Raspberry Pi with a Smart-Metering Extension was presented at the 2nd International Conference on Power Systems and Electrical Technology (PSET) in Milan, Italy, from August 25th to 27, 2023.</p><p></p><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAzwILwxmF3Uy00iz8oyaGJFY3iq5HosUI9zNURFxLJY48oDjJZMHSDjPwUwlAblnLxme7ikD1xTIGGo58jYvqqGa2-roQVi7gP1N65TCpineJDVkToO25IvnbVMEWzMoVtE2Pult2MO-4gC2b0N7vtmBbBU2HM0Wkx80Lr8c13tDNo6qhwywxBpKaO2gO/s2859/yomopie.jpeg" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2279" data-original-width="2859" height="255" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAzwILwxmF3Uy00iz8oyaGJFY3iq5HosUI9zNURFxLJY48oDjJZMHSDjPwUwlAblnLxme7ikD1xTIGGo58jYvqqGa2-roQVi7gP1N65TCpineJDVkToO25IvnbVMEWzMoVtE2Pult2MO-4gC2b0N7vtmBbBU2HM0Wkx80Lr8c13tDNo6qhwywxBpKaO2gO/s320/yomopie.jpeg" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Smart Metering Extension for Raspberry Pi <br /></td></tr></tbody></table>Non-intrusive load monitoring (NILM) is a promising technology for
efficient energy feedback in residential settings, supporting low-cost
energy management systems. However, achieving accurate disaggregation
necessitates higher sampling frequencies than standard smart meters
(15-minute intervals). State-of-the-art methods require a minimum
frequency of 1Hz, increasing system costs and privacy concerns. To
address this, we propose a cost-effective single-device smart meter
utilizing Raspberry Pi and YoMoPie Monitor for efficient and accurate
local processing of user data. <br />Our concept involves a low-cost
single-device smart meter that provides direct feedback based on local
user data processing. The system’s performance was tested in a
laboratory setting under two different scenarios, and promising results
were obtained.<br />Our system demonstrated promising results in
disaggregation performance and computational complexity in laboratory
tests under two scenarios. This study evaluates implementing NILM on an
embedded system with limited resources, achieving satisfactory outcomes
for five appliances. The open-source software and hardware enable easy
replication and further exploration by the research community and other
stakeholders.<p></p><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikLjj6F-8ynlmJGCQ0QugSZnC2IfNDEUU33Aw30QgQxCiQaq7XlbX7X07h3sbfTUeXvinu3wjMNZTlmgZN7_UiO7jN7o5x5W6CGu8qs2QcDVa0KByTdJS3_A25aiwTnQ9a2yhXJL0MltodCPR5AlKjjH1sqL1A4YSuvrTJLOo1OHnEet3ecCkMANVDj4rC/s1254/result3.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="686" data-original-width="1254" height="219" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikLjj6F-8ynlmJGCQ0QugSZnC2IfNDEUU33Aw30QgQxCiQaq7XlbX7X07h3sbfTUeXvinu3wjMNZTlmgZN7_UiO7jN7o5x5W6CGu8qs2QcDVa0KByTdJS3_A25aiwTnQ9a2yhXJL0MltodCPR5AlKjjH1sqL1A4YSuvrTJLOo1OHnEet3ecCkMANVDj4rC/w400-h219/result3.png" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Color indicating detected devices by the NILM algorithm</td><td class="tr-caption" style="text-align: center;"><br /></td><td class="tr-caption" style="text-align: center;"><br /></td><td class="tr-caption" style="text-align: center;"><br /></td><td class="tr-caption" style="text-align: center;"><br /></td><td class="tr-caption" style="text-align: center;"><br /></td></tr></tbody></table> To learn more, check out the paper <br /><p>Johannes Winkler, Hafsa Bousbiat, Stefan Jost, and Wilfried Elmenreich. <a href="https://mobile.aau.at/publications/winkler-2023-Energy_Disaggregation_with_NILM_on_a_Raspberry_Pi_with_Smart-Metering_Extension.pdf">Energy Disaggregation with NILM on a Raspberry Pi with Smart-Metering Extension</a>. In Proc. 2023 2nd International Conference on Power Systems and Electrical Technology (PSET 2023), Milan, Italy, August 25-27, 2023.</p><p>or visit our <a href="https://github.com/smartgrids-aau/NILM_Raspberrypi">NILM Raspberry Pi project on Github</a>.<br /></p><p><br /></p><p></p>Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-35512624347588457862023-06-13T16:14:00.033-07:002023-06-13T16:14:00.149-07:00Unlocking the Full Potential of Neural NILM: On Automation, Hyperparameters & Modular Pipelines<p>Non-Intrusive Load Monitoring (NILM) is a technique used to monitor the energy usage of individual appliances and devices in a home or building, without the need to physically measure each appliance or device. This allows energy managers to more accurately understand how energy is being used in the building. The basic principle behind NILM is to measure the overall energy usage of the building, and then identify patterns in the usage that can be attributed to specific appliances or devices. By analyzing the total energy usage, NILM can identify the type of appliance and its energy consumption. This information can then be used to make informed decisions about energy management, such as identifying energy-efficient appliances and optimizing energy usage. NILM is important for energy management applications because it provides a more comprehensive view of energy use. By understanding the energy usage of individual devices, energy managers can make better decisions about how to optimize energy usage and reduce energy costs. Furthermore, NILM can identify potential problems in the system, such as inefficient appliances, which can be addressed in order to improve efficiency.</p>
<p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjPI_yeg9H5GWeoHkTJxgC8H0WM2W3JpoM8o-z3MNnqcRoS50czkLjZyHkD2XfCzBtQwFs_X1g1wP74pxv0vuElotgK66mBau2q2KUZEfzLbfA1ENUAz7isI_0zJUmHNHkwZhTY0Ij5O4sefBiR8BPz42tGKBOlapWVcyFYPLoLdTHsx20xztZwPrBN1Q/s865/overview_of_the_nilm_pipeline_in_deep_nilmtk.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="335" data-original-width="865" height="177" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjPI_yeg9H5GWeoHkTJxgC8H0WM2W3JpoM8o-z3MNnqcRoS50czkLjZyHkD2XfCzBtQwFs_X1g1wP74pxv0vuElotgK66mBau2q2KUZEfzLbfA1ENUAz7isI_0zJUmHNHkwZhTY0Ij5O4sefBiR8BPz42tGKBOlapWVcyFYPLoLdTHsx20xztZwPrBN1Q/w456-h177/overview_of_the_nilm_pipeline_in_deep_nilmtk.png" width="456" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span dir="ltr" role="presentation" style="font-family: sans-serif; font-size: calc(var(--scale-factor)*7.97px); left: 12.41%; top: 34.58%; transform: scaleX(1.01338);">Overview of the NILM pipeline in</span><span dir="ltr" role="presentation" style="font-family: sans-serif; font-size: calc(var(--scale-factor)*7.97px); left: 38.24%; top: 34.58%;"> </span><span dir="ltr" role="presentation" style="font-family: sans-serif; font-size: calc(var(--scale-factor)*7.97px); left: 38.69%; top: 34.58%; transform: scaleX(1.00539);">Deep-NILMTK</span></td></tr></tbody></table><br />In recent years, Non-Intrusive Load Monitoring (NILM) has become an important tool for identifying the power consumption of individual appliances from a single metering point. Deep learning models are gaining traction in this area, however, there are still many challenges surrounding NILM datasets and the lack of common experimental guidelines. This lack of features and best practices guidelines has limited the adoption of efficient research instruments and made it difficult to compare, replicate, and share results.</p>
<p>To address this problem, we have proposed a novel open-source toolkit, Deep-NILMTK, which leverages the best practices for Deep Learning and offers a common testing bed for NILM algorithms. This toolkit includes a modular NILM pipeline that can be easily customised and introduces the concept of Experiment Templating to improve research efficiency. To demonstrate the effectiveness of the tool, we have created an online NILM benchmark repository and conducted a case-study with eight of the most popular deep NILM algorithms. All sources for the tool are available on Github, along with the accompanying documentation.</p><p>Leveraging this concept and DL best practices, a case-study of creating an online NILM benchmark repository is provided at <a href="https://github.com/BHafsa/DNN-NILM-benchmark">https://github.com/BHafsa/DNN-NILM-benchmark</a> considering eight of the most popular deep NILM algorithms. All sources relative to the tool are publicly available on Github <a href="https://github.com/BHafsa/deep-nilmtk-v1">https://github.com/BHafsa/deep-nilmtk-v1</a> along with the corresponding documentation.</p><p>Further information can be found in the paper</p><p>Hafsa Bousbiat, Anthony
Faustine, Christoph Klemenjak, Lucas Pereira, and Wilfried Elmenreich.
<a href="https://ieeexplore.ieee.org/abstract/document/9889174">Unlocking the
full potential of neural NILM: On automation, hyperparameters & modular
pipelines</a>.
<cite>IEEE Transactions on Industrial Informatics</cite>, pages 1–9, 9 2022.
(<a href="http://dx.doi.org/10.1109/TII.2022.3206322">doi:10.1109/TII.2022.3206322</a>)</p><p> </p>Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-74187136229173554392023-04-10T15:15:00.008-07:002023-04-10T15:15:00.159-07:00A New Unobtrusive Activity Monitoring Framework to Age Safely in the Digital Era<p>In “<a href="https://www.mdpi.com/1424-8220/22/4/1322/pdf">Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances</a>”, Hafsa Bousbiat, Gerhard Leitner and Wilfried Elmenreich suggest a new interactive framework to unobtrusively monitor elderlies’ behavior based on their interaction with electrical appliances involved in their daily activities. Due to the extension of the human lifespan, the economy, societal systems, and healthcare services will be affected. For that reason, technologies were developed to counteract these challenges. One of these would be the Non-Intrusive Load Monitoring (NILM) model to generate energy data on the explicit usage of electric devices. This set of techniques employ smart meters to measure the power consumption of different appliances, which indicate daily routines and thus the well-being of the elderly.</p>
<p>Non-intrusive load monitoring (NILM) is a monitoring technology that can infer the energy consumed by individual appliances within a building by analyzing the total energy consumption of the building. This technology dates back to work done by George Hart in the 1990s and has since been developed further. The Smart Grids Group of the <a href="https://nes.aau.at/">Institute of Networked and Embedded Systems</a> has a long-standing experience in developing and using NILM technologies, with their work spanning from fundamental research to practical applications of the technology.</p>
<p>The research work is a collaboration between three institutes <span data-offset-key="c7p7v-323-0"><span data-offset-key="c7p7v-323-0"><span data-text="true">(</span></span></span><a href="https://www.aau.at/en/digital-age-research-center/"><span data-offset-key="c7p7v-324-0"><span data-offset-key="c7p7v-324-0"><span data-text="true">D</span></span></span></a><span data-offset-key="c7p7v-325-0"><span data-text="true"><a href="https://www.aau.at/en/digital-age-research-center/">igital Age Research Center (D!ARC)</a>, <a href="https://nes.aau.at/">Institute of Networked and Embedded Systems (NES)</a>, and the <a href="https://www.aau.at/en/isys/">Department of Information Systems (ISYS</a>))</span></span> at the <a href="https://www.aau.at/">University of Klagenfurt</a> and is part of the dissertation project of Hafsa Bousbiat, a promising young female researcher who is part of the DECIDE doctoral school. </p><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjj00zg_ZMrf9xLN1_-VdICmtYkm7mfM6K8Xgz_Wi2I4v1MmHvYriRG7dexS6B8LBOUep2shJJEc2IFUdDq-TSRKEXTrYDHmWDSu-Ry2uAorkGmr6VBSR9sXMpXwYARAtPk_cRrbZKpEnp4EDwXs_MRhLOj5vK_CnD71u8svBn0WAWGLqPgCkMIPaSwNA/s1301/Activity%20Monitoring%20Framework%20Overview.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="481" data-original-width="1301" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjj00zg_ZMrf9xLN1_-VdICmtYkm7mfM6K8Xgz_Wi2I4v1MmHvYriRG7dexS6B8LBOUep2shJJEc2IFUdDq-TSRKEXTrYDHmWDSu-Ry2uAorkGmr6VBSR9sXMpXwYARAtPk_cRrbZKpEnp4EDwXs_MRhLOj5vK_CnD71u8svBn0WAWGLqPgCkMIPaSwNA/w488-h180/Activity%20Monitoring%20Framework%20Overview.png" width="488" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Overview of the proposed activity monitoring framework</td></tr></tbody></table><p>The paper suggests a new activity monitoring framework based on hand-operated appliances inferred from energy data and discusses two case studies based on their pipeline, including NILM approaches and their effect on activity monitoring. The framework includes a load disaggregation module, an activity monitoring module, and a feedback management module. These modules measure the aggregated power in a household, provide contextual and operational information on the condition of the devices and detect anomalies. It also includes feedback from external agents to overall create a more accurate understanding of recent patterns and routines of the occupants with the help of anomaly detection techniques.</p><p></p>
<p>Further information can be found in the paper:</p><p>H. Bousbiat, G. Leitner,
and W. Elmenreich.
<a href="https://www.mdpi.com/1424-8220/22/4/1322/pdf">Ageing safely in the
digital era: A new unobtrusive activity monitoring framework leveraging on
daily interactions with hand-operated appliances</a>.
<cite>Sensors</cite>, 22(4), 2022.
(<a href="http://dx.doi.org/10.3390/s22041322">doi:10.3390/s22041322</a>) <br /></p>
Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-37766476241482913352023-03-09T16:09:00.001-08:002023-03-09T16:09:42.678-08:00Neural NILM Learning Paradigms: From Centralised to Decentralised Learning
<p><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxltmMh_HHo7sm8Du4cWSt6UtmKIic4g--bmoqG1t9Qpp40lwAsvqzCpgyifxV0uatSpfLF-f84pZI1_AWKSoJXKLS0Qtg4sGEJI1HD-RdejtmyAFfBTZqhBSd9BqwOr_dbN-MTs06vDyioy5PLoceE9GlcV8csJV3gqXaMwBIaKhuWv6WFHFmCB6Xww/s600/Centralised_vs_collaborative_learning.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="470" data-original-width="600" height="251" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxltmMh_HHo7sm8Du4cWSt6UtmKIic4g--bmoqG1t9Qpp40lwAsvqzCpgyifxV0uatSpfLF-f84pZI1_AWKSoJXKLS0Qtg4sGEJI1HD-RdejtmyAFfBTZqhBSd9BqwOr_dbN-MTs06vDyioy5PLoceE9GlcV8csJV3gqXaMwBIaKhuWv6WFHFmCB6Xww/s320/Centralised_vs_collaborative_learning.png" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">C<span dir="ltr" role="presentation" style="font-family: sans-serif; font-size: calc(var(--scale-factor)*9.96px); left: 57.02%; top: 30.43%; transform: scaleX(0.941912);">entralised vs collaborative learning</span></td></tr></tbody></table>Non-intrusive Load Monitoring (NILM) has become a paramount in both industrial and residential sectors to achieve efficient energy consumption. Deep neural networks have been gaining the highest interest from the research community, commonly referred to as neural NILM. In most cases, neural NILM models follow a centralised based learning scheme, where the energy data is assumed to be available in a central node for training. This practice can, however, raise privacy and security concerns from the consumer’s side since energy data can reveal in-home activities and occupancy records if intercepted. In response, Federated Learning (FL) has been suggested as a viable solution to address these issues. In the paper "Neural NILM Learning Paradigms: From Centralised to Decentralised Learning", an overview of neural NILM models following both a centralised and a federated learning paradigm was presented while also identifying the main challenges with regard to both learning paradigms and potential future research directions for more robust, secure and privacy-preserving models in the neural NILM industry. Overall, as any other new technology, FL has its merits and limitations. Typically, FL provides promising perspectives to solve the privacy issues of energy disaggregation. However, it also opens doors for new challenges, especially those related to the (i) low disaggregation performance of FL-based NILM algorithms, (ii) susceptibility to noise, (iii) lack of labeled sub-metered data at the customer’s level, and (iv) need to adopt robust security mechanisms.<br /></p><p>
</p><p>Further information can be found in the paper:</p><p>Hafsa Bousbiat, Christoph Klemenjak,
Yassine Himeur, Wilfried Elmenreich, Abbes Amira, Wathiq Mansoor, and Shadi
Atalla.
<a href="http://dx.doi.org/10.1109/icspis57063.2022.10002485">Neural NILM
learning paradigms: From centralised to decentralised learning</a>.
In <cite>Proceedings of the 2022 5th International Conference on Signal
Processing and Information Security (ICSPIS)</cite>, pages 138–142, December
2022.
(<a href="http://dx.doi.org/10.1109/icspis57063.2022.10002485">doi:10.1109/icspis57063.2022.10002485</a>)</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgmjxOe6PG88BmBWzVNG32RycZXt6vDnUvq3d4kd-Dzl2OrCMDbP8UApOuLuhjKqs3huvdiioA6tv3HUB9DVfOxgQxWnOrdEFddhlozaMft7vwFnrR5dx04idzm9XKcnD0vyawu4p5L6vn3n1gVpwMDBnSRA6O-QN1uUvP_MYn9uLF4RqOQhVJk17f7cQ/s1024/Best%20Paper%20Award%20ICSPIS.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="470" data-original-width="1024" height="147" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgmjxOe6PG88BmBWzVNG32RycZXt6vDnUvq3d4kd-Dzl2OrCMDbP8UApOuLuhjKqs3huvdiioA6tv3HUB9DVfOxgQxWnOrdEFddhlozaMft7vwFnrR5dx04idzm9XKcnD0vyawu4p5L6vn3n1gVpwMDBnSRA6O-QN1uUvP_MYn9uLF4RqOQhVJk17f7cQ/s320/Best%20Paper%20Award%20ICSPIS.png" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The paper also won the best paper award at the 5th International
Conference on Signal Processing and Information Security (ICSPIS) in
December 2012.</td></tr></tbody></table><br /><p><br /></p><p></p><p></p>Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-55503330315578346042023-03-02T12:23:00.009-08:002023-03-22T13:26:55.154-07:00The Role of Renewable Energies in the Arctic <p><span data-offset-key="1st4v-150-0"><span data-text="true">Last week, Prof. David Finger from Sustainability Institute and Forum at Reykjavik University visited the University of Klagenfurt as a guest researcher. His inspiring talk at Energy Cluster Meeting XXXI, titled "Climate-Neutral Europe: the Role of Renewable Energies in the Arctic to decarbonize Europe and enhance energy independence", was truly captivating and gave us all a glimpse into the possibilities of an Austrian-Icelandic Energy Cooperation. </span></span></p><p><span data-offset-key="1st4v-150-0"><span data-text="true"></span></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhcsJtAibsCeVwiymQVKwyWY0qJG40R4bnUoXDqGkJe66IvBGB8h9aHOoMG99fe4jvKFyb47Z5DUpi97J6t7NsWa9wrR3sJJrZoUr37tDGbTzokp9TDQGh82tB3FUJ7Y_TLMuzJcS7noSWUFEEh8cT1rFcbDMQkDtBNXaq-6izcRmE6mmWyHN7SHeTwUQ/s3374/finger_iceland_2023.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="2228" data-original-width="3374" height="211" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhcsJtAibsCeVwiymQVKwyWY0qJG40R4bnUoXDqGkJe66IvBGB8h9aHOoMG99fe4jvKFyb47Z5DUpi97J6t7NsWa9wrR3sJJrZoUr37tDGbTzokp9TDQGh82tB3FUJ7Y_TLMuzJcS7noSWUFEEh8cT1rFcbDMQkDtBNXaq-6izcRmE6mmWyHN7SHeTwUQ/s320/finger_iceland_2023.jpg" width="320" /></a></div>Students and researchers alike were amazed by the future of renewable energy in Europe that Prof. Finger's talk highlighted and left the room with interesting insights into the role of renewable energy in the Arctic. It was a great opportunity to learn more about the progress of climate neutrality and energy independence in Europe and we look forward to further collaborations with Prof. Finger in the future.<br /><p></p>Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-21138808546085656572023-03-01T08:27:00.001-08:002023-03-01T08:27:00.159-08:00Energy Informatics 2023 in Vienna -- Call for Papers<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiYRPjUVOoVH3tyaEZyJ93kG4SX6phJ-fVoHH2h4eMorj1kASNiMbWahgUDl7corNcU4GlSpPFvQTaiQ-9gfog-qTjfzdlIkYeVkT7iGoE4vxPMJQk0lZsBkVePHKYBIQZmSYfDWvRon41BNewn3B0LQBl6ZchRJaeQT7uxN-4xGqWX9stPtvhaVaEEiQ/s1200/Wien_Stephansdom.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="1200" data-original-width="800" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiYRPjUVOoVH3tyaEZyJ93kG4SX6phJ-fVoHH2h4eMorj1kASNiMbWahgUDl7corNcU4GlSpPFvQTaiQ-9gfog-qTjfzdlIkYeVkT7iGoE4vxPMJQk0lZsBkVePHKYBIQZmSYfDWvRon41BNewn3B0LQBl6ZchRJaeQT7uxN-4xGqWX9stPtvhaVaEEiQ/s320/Wien_Stephansdom.jpg" width="213" /></a></div>The EU aims to be climate-neutral by 2050 – an economy with net-zero
greenhouse gas emissions. This objective is at the heart of the European
Green Deal and in line with the EU’s commitment to global climate
action under the Paris Agreement. The transition to a climate-neutral
society is both an urgent challenge and an opportunity to build a better
future for all. Energy informatics support in solving many challenges
of the energy transition, by providing solutions for intelligent
management and operation of energy systems and their assets.<p></p>
<p>The objective of the DACH+ conference series on Energy Informatics is
to promote research, development, and implementation of information and
communication technologies in the energy domain and to foster the
exchange between academia, industry, and service providers in the
German-Austrian-Swiss region and its neighbouring countries (DACH+).</p>
<p>We seek high-quality original contributions addressing the
design, adoption, operation and management of smart energy systems, the
integration of intermittent renewable generation and energy efficiency
gains through ICT, market approaches and mechanisms for ICT-enabled
energy systems, and research on associated (decentralised) data-driven
decisions. We welcome theoretical contributions as well as publications
addressing system design, implementation, and experimentation. The list
of topics of interest to the conference includes, but is not limited to:</p>
<ul><li>ICT for future energy systems, sector coupling and the integration of intermittent renewable generation</li><li>Information and decision support systems for future energy markets and mechanisms</li><li>Energy system modelling and (open) energy system data</li><li>Protocols and architectures for IT systems in the energy sector</li><li>Data analytics and machine learning for smart energy systems and
decentralised decision-making, as well as platforms for data analysis</li><li>Open data and software for energy research</li><li>Management of distributed generation and demand side management</li><li>ICT for (multi-) energy networks and micro-grids</li><li>Energy-efficient mobility, charging management for electric
vehicles, energy-aware traffic control, and smart grid integration of
mobile storage</li><li>Smart buildings, digital metering, occupant comfort, and user interaction</li><li>Adoption of ICT in the energy sector</li><li>Cross-cutting issues including cyber security and privacy
protection, interoperability, verification of networked smart grid
systems</li></ul><h2><strong>Posters, Demos and Workshops</strong></h2>
<p>Submissions for posters, demos, and workshop suggestions are welcome,
too. The topics of interest are the same as indicated above. Posters
and demos require the submission of an extended abstract, which will be
peer-reviewed. If accepted, the abstract will appear in the conference
proceedings. Further details can be found on the conference website.</p>
<h2><strong>Submission and Publication</strong></h2>
<p>Submitted papers will be reviewed in a double-blind process. Accepted
and presented papers will be published in the Springer Open Journal <em>Energy Informatics</em> (<a href="https://energyinformatics.springeropen.com">https://energyinformatics.springeropen.com</a>).
The conference language is English, and papers must be written in
English. We solicit full research papers (max. 18 pages of content plus 2
additional pages for references) as well as short papers (max. 10 pages
of content plus 2 additional pages for references). Templates and
instructions will be made available at <a href="http://www.energy-informatics.eu/">http://www.energy-informatics.eu/</a>.
Further information on the submission of posters and demos is also
available on the website. The Open Access fee for the journal article is
included in the registration fee.</p><h3><strong>Important dates</strong></h3>
<p>Apr 09, 2023: Submission of papers</p>
<p>May 16, 2023: Decision acceptance (assignment of shepherds) / rejection</p>
<p>May – Aug Incremental revision process between author and shepherd</p>
<p>Jul 02, 2023: Camera-ready deadline for poster abstracts for accepted contributions</p>
<p>Oct 04 2023: 14th Doctoral Workshop Energy Informatics</p>
<p>Oct 05-06, 2023: 12th DACH+ Conference on Energy Informatics</p>Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-88489478629486523582023-02-23T08:25:00.001-08:002023-02-23T08:25:35.828-08:00Energy to train AI tools, wasted?<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjWxDzc2B7reFZFB-vWhzFtKzFwMvFS72SiGrLWvrxQ-94s4f7wksDCoz5G_5O1tDqtim3yKwWUgCAAxaU8gpguHs3RYVr7YaDGmQztlWYwfOGjVDqyQMSH3but4M3RZwCe9U0XaWQcIn8vBM3CXFvkp3_WxlhvnLmiBbHpnQ4N3HiGzHSHB7e9AHyNEQ/s511/burning_fossil_fuels.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="511" data-original-width="511" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjWxDzc2B7reFZFB-vWhzFtKzFwMvFS72SiGrLWvrxQ-94s4f7wksDCoz5G_5O1tDqtim3yKwWUgCAAxaU8gpguHs3RYVr7YaDGmQztlWYwfOGjVDqyQMSH3but4M3RZwCe9U0XaWQcIn8vBM3CXFvkp3_WxlhvnLmiBbHpnQ4N3HiGzHSHB7e9AHyNEQ/w200-h200/burning_fossil_fuels.png" width="200" /></a></div>Energy used to make and provide online services is an important
consideration for many reasons. Production and delivery of online
services require energy, and that energy has a direct impact on the
environment. The energy used to create and provide online services often
comes from burning fossil fuels, such as coal, natural gas, and oil.
This burning releases carbon dioxide (CO2) and other pollutants into the
atmosphere, contributing to global warming. Burning fossil fuels also
releases other harmful pollutants, such as particulate matter, sulfur
dioxide, and nitrogen oxides, contributing to air pollution and can
cause serious health problems. Increased energy consumption also has a
direct effect on our environment. As energy consumption increases, so
does the demand for resources such as coal, natural gas, and oil. This
can lead to the destruction of ecosystems and habitats, as well as the
displacement of communities. Additionally, burning these resources to
produce energy contributes to climate change, causing a shift in
weather patterns, rising sea levels, and an increase in extreme weather
events. The energy used to provide online services also has an impact on
the cost of providing these services. The more energy used to power the
servers and networks, the more expensive the services become.
Additionally, higher energy costs can lead to higher consumer prices, as
companies must pass on the extra costs to their customers. Finally,
suppose energy used to provide online services is generated from
non-renewable sources, such as coal and oil. In that case, it means that
the energy used to power these online services will eventually run out,
which could negatively impact the availability of these services in the
future. Overall, it is essential to consider the energy used to make
and provide online services. Burning fossil fuels to power these
services contributes to air pollution and global warming while also
increasing costs. Additionally, the use of non-renewable resources to
generate energy could lead to a decrease in the availability of these
services in the future. <p></p><p>A prominent example of online services is
AI chatbots that can provide the user with answers to almost any topic.
Other than a search engine that only finds matches of the search text
in the indexed documents, AI chatbots can compose new information by
drawing connections between the vast amount of information they have
been trained with. AI programs like ChatGPT are a highly relevant
development because they significantly improve the user experience and
enable people from all domains to access sophisticated AI technology.
Open AI programs make AI more accessible, allowing developers to share
and collaborate on AI models. It also helps reduce development costs and
makes integrating AI into existing applications easier. By allowing
developers to access and build upon existing models, they can create new
and innovative applications that can benefit everyone. Developing AI
models helps automate tedious tasks and reduce the time spent on manual
labor. By using AI models, businesses can automate mundane tasks and
improve their workflow. AI models can also help to improve customer
support and increase customer satisfaction. AI models are also important
for predicting future trends and predicting customer behavior.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjci2CKnSzT82IV9Fyit-NvXsI1hRQNBB6cJgVik-aZPzBh00IrtwSYgBI3Rcec_5ap6TIGUf3vDdFbzF1wqJ0MO5awDxzqkxJdT817_w9C-q8hre3o3cei0WFmiVv6sAruEb135WRyN0M_vAAV4tTkWoW27YSrA93mZsqOB_Nt5xT9bUA3lP4ylenNUg/s513/ai-monk-bookwriter.png" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="513" data-original-width="513" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjci2CKnSzT82IV9Fyit-NvXsI1hRQNBB6cJgVik-aZPzBh00IrtwSYgBI3Rcec_5ap6TIGUf3vDdFbzF1wqJ0MO5awDxzqkxJdT817_w9C-q8hre3o3cei0WFmiVv6sAruEb135WRyN0M_vAAV4tTkWoW27YSrA93mZsqOB_Nt5xT9bUA3lP4ylenNUg/s320/ai-monk-bookwriter.png" width="320" /></a></div><p style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;"></p><p style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;"><span data-preserver-spaces="true" style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;">But,
despite the fact that users of AI often get free access or a generous
free trial, developing and training an AI model does not come for free
when we consider the energy budget. The Carbon footprint of training
ChatGPT has been estimated to be 1287 MWh [1], in addition to running
the services. Are 1287 MWh a number to be concerned with? Probably yes.
Is it a number so high that we immediately need to banish AI training
for the sake of the environment? I don't think so.</span></p><p style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;"><span data-preserver-spaces="true" style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;">When
relating 1287 MWh to a single person, it is a lot. It would mean
driving an average European car on fossil fuels for 4,5 Mio km. That is
enough to travel the whole road network of the United States or
equivalent to the carbon footprint of a flight passenger going form
London to New York 320 times.</span></p><p style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;"><span data-preserver-spaces="true" style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;">Nevertheless,
ChatGPT has more than one user. In fact, it is one of the
fastest-growing online platforms in the world, with around 100 Million
users at the time of writing. Dividing the development costs by the
users, it amounts to 0.01287 kWh or roughly 1% of the energy required to
print a book. </span></p><p style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;"><span data-preserver-spaces="true" style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;">In
other words, if users can utilize the AI system to automate mundane
tasks and improve their workflow, the energy spent on creating the AI is
probably well-invested. Many usages are recreational, and sometimes the
AI provides more fiction than facts, but so is the case with books.</span></p><p style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;"><span data-preserver-spaces="true" style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;">However, we need to keep our eyes open on two issues:</span></p><ul style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;"><li style="background: transparent; color: #0e101a; list-style-type: disc; margin-bottom: 0pt; margin-top: 0pt;"><span data-preserver-spaces="true" style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;">The
operational cost of running the system: "Cost" would mean here energy
cost as well as financial cost. If the system does not work here
efficiently, we could end up in a much higher energy waste than 1287 MWh</span></li><li style="background: transparent; color: #0e101a; list-style-type: disc; margin-bottom: 0pt; margin-top: 0pt;"><span data-preserver-spaces="true" style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;">Further
developments in training new AIs: competitors might train their own
AIs, no matter the (energy) cost. And models are expected to grow in
complexity and capabilities, probably also significantly raising the
energy required for training a single model.</span></li></ul><span data-preserver-spaces="true" style="background: transparent; color: #0e101a; margin-bottom: 0pt; margin-top: 0pt;">So let's keep an eye on further developments. <br /></span><p>[1]
Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia,
L.-M., … Dean, J. (4 2022). The Carbon Footprint of Machine Learning
Training Will Plateau, Then Shrink. Computer, 55, 18–28. Retrieved from
http://arxiv.org/abs/2204.05149<br /></p>Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-15431308996260284362022-01-20T02:16:00.002-08:002022-01-20T02:16:45.759-08:00Energy Informatics 2022 -- Call for Papers<div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEj07gA5ZPW5G-oUSTalvoBQ_uqqI6QZzx-yA6XS1sCu-tq2eu-M1zc660ciV5V5hE-PjWm4TvfAcTUUZUQ92bQosSz5s6ejszY2AtBWvLVHctWnEEWocr2tf4Suwl4PztLg9BWVOj0WPzTERmNKO6NcckejvuzR71UpgVstWhGXGHeA3pAnOtxJMESt6g=s870" style="clear: right; display: block; float: right; padding: 1em 0px; text-align: center;"><img alt="" border="0" data-original-height="870" data-original-width="526" height="320" src="https://blogger.googleusercontent.com/img/a/AVvXsEj07gA5ZPW5G-oUSTalvoBQ_uqqI6QZzx-yA6XS1sCu-tq2eu-M1zc660ciV5V5hE-PjWm4TvfAcTUUZUQ92bQosSz5s6ejszY2AtBWvLVHctWnEEWocr2tf4Suwl4PztLg9BWVOj0WPzTERmNKO6NcckejvuzR71UpgVstWhGXGHeA3pAnOtxJMESt6g=s320" /></a></div>Countries worldwide have strengthened their commitment to
decarbonize the energy system at COP26 in Glasgow. This intent
necessitates a massive increase in renewable power generation and
the electrification of energy demand through sector coupling
(e.g., heat and mobility). Over the last year, for example, the
DACH countries have created roadmaps to establish hydrogen as an
additional sustainable energy carrier. Energy informatics
contributes to solving many of the challenges of this energy
transition, by connecting various decentralized resources and by
operating them intelligently.
<p>The objective of the DACH+ conference series on Energy
Informatics is to promote research, development, and
implementation of information and communication technologies in
the energy domain and to foster the exchange between academia,
industry, and service providers in the German-Austrian-Swiss
region and its neighbouring countries (DACH+).<br />
</p>
<p>We seek high-quality original contributions addressing the
design, adoption, operation and management of smart energy
systems, the integration of intermittent renewable generation
and energy efficiency gains through ICT, market approaches and
mechanisms for ICT-enabled energy systems, and research on
associated (decentralised) data-driven decisions. We welcome
theoretical contributions as well as publications addressing
system design, implementation, and experimentation. The list of
topics of interest to the conference includes, but is not
limited to:<br />
</p>
<ul><li>ICT for future energy systems, sector coupling and the
integration of intermittent renewable generation</li><li>Information and decision support systems for future energy
markets and mechanisms</li><li>Energy system modelling and (open) energy system data<br />
</li><li>Protocols and architectures for IT systems in the energy
sector</li><li>Data analytics and machine learning for smart energy systems
and decentralised decision making, as well as platforms for
data analysis</li><li>Open data and software for energy research</li><li>Management of distributed generation and demand side
management</li><li>ICT for (multi-) energy networks and micro-grids</li><li>Energy-efficient mobility, charging management for electric
vehicles, energy-aware traffic control, and smart grid
integration of mobile storage</li><li>Smart buildings, digital metering, occupant comfort, and
user interaction</li><li>Adoption of ICT in the energy sector</li><li>Cross-cutting issues including cyber security and privacy
protection, interoperability, verification of networked smart
grid systems</li></ul>
<p><b>Important Date </b></p>
<ul><li>Paper, poster & demo submission: Apr 8, 2022</li><li>Notification: Jun 6, 2022</li><li>Camera-ready paper due: Jun 24, 2022</li><li>Doctoral Workshop: Sep 13-14, 2022</li><li>Conference: Sep 15-16, 2022</li></ul>
<p><strong>Posters, Demos and Workshop </strong></p><p><strong> </strong><br />
Submissions for posters, demos, and workshop suggestions are
welcome, too. Details can be found on the conference website.<br />
<br />
<strong>Submission and Publication </strong></p><p><strong> </strong><br />
Submitted papers will be reviewed in a double-blind process.
Accepted and presented papers will be published in the Springer
Open Journal Energy Informatics <a class="moz-txt-link-freetext" href="https://energyinformatics.springeropen.com" rel="noopener noreferrer" target="uPDyWgi8TojwC8niXsfoyya">https://energyinformatics.springeropen.com</a>.
The conference language is English, and papers must be written
in English. We solicit full research papers (max. 18 pages of
content plus 2 additional pages for references) as well as short
papers (max. 10 pages of content plus 2 additional pages for
references). Templates and instructions will be made available
at <a class="moz-txt-link-freetext" href="https://www.energy-informatics.eu/" rel="noopener noreferrer" target="lOe0CrJHTq0qfPKeNzyVgBS">https://www.energy-informatics.eu/</a>.
Further information on the submission of posters and demos is
also available on the website. The Open Access fee for the
journal article is included in the registration fee.</p>Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0Germany48.00445 7.8403747.998707410171079 7.8317869311523438 48.010192589828918 7.8489530688476563tag:blogger.com,1999:blog-2054684438136045652.post-77825020175881764882021-01-05T07:43:00.001-08:002021-01-05T09:25:42.541-08:00Investigating the impact of data quality on the energy yield forecast using data mining techniques<p>In this paper, we analysed the impact of using optimum combination of input variables and low dimensional subspace on Photovoltaic (PV) production forecasting accuracy. We worked in collaboration with Prof. Mussetta from Politecnico di Milano.</p><div style="text-align: justify;">The main contribution presented in the paper is divided in two parts:</div><div style="text-align: left;"><ol style="text-align: left;"><li style="text-align: justify;">Optimum combination of input meteorological features using feature extraction technique</li><li style="text-align: justify;">Low dimensional subspace using dimensional reduction technique</li></ol><div style="text-align: justify;">We assess and compare two cases when forecasting models are fed with all the features with the case when low subspace of dataset is used as an input to the models.</div><div style="text-align: justify;">The simulation results reveal that depending on the location under study and the regression methods, using less variables as input to the forecasting models are enough to generate nearly similar results without affecting the performance. However, it is necessary to conduct the tests under different climatic conditions so as to ensure the reliability of the results. </div><div style="text-align: justify;">The figures below show the results obtained applying Pearsons correlation and principal component analysis. Figure 1 represents strength of association between two variables. Figure 2 is the biplot representation of the input features contributing variance on principal components PC1 and PC2.</div><div style="text-align: justify;"><br /></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhubY7eiqTfIPqwP0NAn5q5VQ7siXtwfwZZtDvn4xWi0-AV3y56_FbzNwMTmjT8_HiOHz50aAvJSAWCf-XTHysbmh0eYsxhSjkbsSTnMmWzCLP6xIqf72RQXcflqb0V4CMmU_-Fc1CcAgk/s882/CorrelationMap+%25281%2529.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="755" data-original-width="882" height="352" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhubY7eiqTfIPqwP0NAn5q5VQ7siXtwfwZZtDvn4xWi0-AV3y56_FbzNwMTmjT8_HiOHz50aAvJSAWCf-XTHysbmh0eYsxhSjkbsSTnMmWzCLP6xIqf72RQXcflqb0V4CMmU_-Fc1CcAgk/w411-h352/CorrelationMap+%25281%2529.png" width="411" /></a></div><div class="separator" style="clear: both; text-align: center;">Fig.1 : Pearson correlation map</div><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhZqvLCTGtUuFxICr0DLF1SxOMq4JPgLhGowlOZcK0N_Vb1qs2GYaI1zxxOss3zEsLPtDYUfopo3lQK3zG6sdzVKfHuvuFnY2AjmEpNU1yUER5B2GMpiTH001bFkipCQpmFhePSV0Nag0Y/s955/PCABiplot.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="621" data-original-width="955" height="283" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhZqvLCTGtUuFxICr0DLF1SxOMq4JPgLhGowlOZcK0N_Vb1qs2GYaI1zxxOss3zEsLPtDYUfopo3lQK3zG6sdzVKfHuvuFnY2AjmEpNU1yUER5B2GMpiTH001bFkipCQpmFhePSV0Nag0Y/w435-h283/PCABiplot.png" width="435" /></a></div><div class="separator" style="clear: both; text-align: center;"><span style="background-color: white; color: #333333; font-family: Georgia, serif; font-size: 15px; text-align: start;"><br /></span></div><div class="separator" style="clear: both; text-align: center;">Fig.2 : Biplot representation </div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div style="text-align: justify;">ISGT Europe 2020 was held virtually and we recorded the presentation for the same. The presentation is available at <a href="https://www.loom.com/share/33cc143f2a614ec5a8ca00e2cd2514db">this</a> link. <br /></div></div><div style="text-align: left;"><br /></div><p></p><p>
</p><p></p><p></p><p style="text-align: center;"><iframe allowfullscreen="" frameborder="0" height="313" mozallowfullscreen="" src="https://www.loom.com/embed/33cc143f2a614ec5a8ca00e2cd2514db" webkitallowfullscreen="" width="500"></iframe> </p><p>To support reproducibility and validating the results we have
released the dataset utilized in the work along with the codes. </p><p><a href="https://github.com/Ekanki/energy-forecasting">Github repository with used dataset and evaluation code</a> </p><p>For more information please see the paper:</p>
<p>Ekanki Sharma, Marco Mussetta, and Wilfried Elmenreich. <a href="https://mobile.aau.at/publications/sharma-2020-investigating_the_impact_of_data_quality_on_the_energy_yield_forecast_using_data_mining_techniques.pdf">Investigating the impact of data quality on the energy yield forecast using data mining techniques</a>. In Proceedings of the IEEE PES Innovative Smart Grid
Technologies Europe (ISGT-Europe). IEEE, October 2020.</p><p> </p><gdiv id="ginger-floatingG-container" style="left: 0px; position: absolute; top: 0px;"><gdiv class="ginger-floatingG ginger-floatingG-closed" style="display: none;"><gdiv class="ginger-floatingG-disabled-main"><gdiv class="ginger-floatingG-bar-tool-tooltip">Enable Ginger</gdiv></gdiv><gdiv class="ginger-floatingG-offline-main"><gdiv class="ginger-floatingG-bar-tool-tooltip"><i>Cannot connect to Ginger</i> Check your internet connection<br /> or reload the browser</gdiv></gdiv><gdiv class="ginger-floatingG-enabled-main"><gdiv class="ginger-floatingG-bar"><gdiv class="ginger-floatingG-bar-tool ginger-floatingG-bar-tool-disable"><ga></ga><gdiv class="ginger-floatingG-bar-tool-tooltip">Disable in this text field</gdiv></gdiv><gdiv class="ginger-floatingG-bar-tool"><ga class="ginger-floatingG-bar-tool-edit">Edit</ga><gdiv class="ginger-floatingG-bar-tool-tooltip">Edit in Ginger</gdiv></gdiv><gdiv class="ginger-floatingG-bar-tool ginger-floatingG-bar-tool-mistakes"><ga><span class="ginger-floatingG-bar-tool-mistakes-count"></span></ga><gdiv class="ginger-floatingG-bar-tool-tooltip">Edit in Ginger</gdiv></gdiv></gdiv></gdiv><gdiv class="ginger-floatingG-contentPopup"><gdiv class="ginger-floatingG-contentPopup-wrap"><ga class="ginger-floatingG-contentPopup-close">×</ga><gdiv class="ginger-floatingG-contentPopup-frame"><iframe scrolling="no"></iframe></gdiv></gdiv></gdiv></gdiv></gdiv>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-79989549613342856942020-11-16T00:00:00.008-08:002020-11-16T00:00:02.612-08:00Investigating the Benefit of Time-Series Imaging for Load Disaggregation<p>In this paper, we investigate the benefits of time-series imaging in load disaggregation, as we augment the wide-spread sequence-to-sequence approach by a key element: <u>an imaging block</u>. </p><p><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjG3CeJKPT4kcvC8yCUOtUMTBHeAIx025XC9GKGUiqgCJSmI8BBAGc657DH_w1hiuHQOEDsB2u8W5oxvFgnpJ5b36iPGtOeSHVFaNIhU1aPrdHQEK0tCoQ92VGZF7H0ys-_umi6Ovwy9yMF/s174/Rp_examples740.gif" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="161" data-original-width="174" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjG3CeJKPT4kcvC8yCUOtUMTBHeAIx025XC9GKGUiqgCJSmI8BBAGc657DH_w1hiuHQOEDsB2u8W5oxvFgnpJ5b36iPGtOeSHVFaNIhU1aPrdHQEK0tCoQ92VGZF7H0ys-_umi6Ovwy9yMF/s16000/Rp_examples740.gif" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A Recurrence Plot<br /></td></tr></tbody></table>The approach presented in this paper converts an input sequence to an image, which in turn serves as input to a modified version of a common Denoising Autoencoder architecture used in load disaggregation. Based on these input images, the Autoencoder estimates the power consumption of a particular appliance. </p><p>The main contribution presented in this paper is a comparison study of three common imaging techniques: </p><p></p><ul style="text-align: left;"><li><u>Gramian Angular Fields, </u></li><li><u>Markov Transition Fields, </u></li><li><u>Recurrence Plots</u>.</li></ul>Further, we assess the performance of our augmented networks by a comparison with two benchmarking implementations, one based on Markov Models and the other one being a common Denoising Autoencoder. ´<p></p><p>The outcome of our study reveals that in <u>19 of 24 cases</u>, the considered augmentation techniques provide improved performance over the baseline implementation. Further, the findings presented in this paper indicate that the Gramian Angular Field could be better suited, though the Recurrence Plot was observed to be a viable alternative in some cases. </p><p>Our paper is to appear at the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’20):</p><blockquote><p>Hafsa Bousbiat, Christoph Klemenjak, and Wilfried Elmenreich. 2020. <a href="https://klemenjak.github.io/publication/2020-10-20-imaging" target="_blank">Exploring Time Series Imaging for Load Disaggregation</a>. In The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’20), November 18–20, 2020, Virtual Event, Japan.</p></blockquote><p> We are looking forward to discussing our paper at BuildSys!</p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-88658753007142298162020-10-20T05:45:00.001-07:002020-10-20T05:45:07.381-07:00Evolving NILM to NIAD: Non-Intrusive Activity Detection<p>Almost all documented practical use cases of load disaggregation rely on the analysis of appliance operational times and their impact on the monthly electricity bill. However, load disaggregation bears promising potential for other use cases. Recognizing user activities without the need to set up a dedicated sensing infrastructure is one such application, given that many household activities involve the use of electrical appliances. State-of-the-art disaggregation algorithms only provide support for the recognition of one appliance at a time, however. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh_H-37GuDG4okG1PdQxo8OaGOo2iRBcP5sv0b6V4ZTC_OcwGUvovPcmyB9hq9nmUYP392ZW0DbgYW7qVGdZWLfN-1JkyvxIjLagGy_WkN1jzD2cQ5CIja1vENyXhCa-I7UAmh-tB6H54eS/s405/drawings-of-everyday-life.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="166" data-original-width="405" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh_H-37GuDG4okG1PdQxo8OaGOo2iRBcP5sv0b6V4ZTC_OcwGUvovPcmyB9hq9nmUYP392ZW0DbgYW7qVGdZWLfN-1JkyvxIjLagGy_WkN1jzD2cQ5CIja1vENyXhCa-I7UAmh-tB6H54eS/s320/drawings-of-everyday-life.jpg" width="320" /></a></div><p>In collaboration with <a href="https://www.areinhardt.de/" target="_blank">Andreas Reinhardt</a> from TU Clausthal, we thus take load disaggregation to the next level, and present to what extent it is applicable to monitor user activities involving multiple appliances (operating sequentially or in parallel) using this technique. For the evaluation of our <u>Non-Intrusive Activity Detection (NIAD)</u>, we synthetically generate load signature data to model nine typical user activities, followed by an assessment to what extent they can be detected in aggregate electrical consumption data. Our results prove that state-of-the-art load disaggregation algorithms are also well-suited to identify user activities, at accuracy levels comparable to (but slightly below) the disaggregation of individual appliances.</p><p>Our paper is to appear at the 2nd ACM Workshop on Device-Free Human Sensing (DFHS'20):</p><blockquote><p>Andreas Reinhardt and <u>Christoph Klemenjak</u>. 2020. <a href="https://klemenjak.github.io/publication/2020-10-13-niad" target="_blank">Device-Free User Activity Detection using Non-Intrusive Load Monitoring: A Case Study</a>. In The 2nd ACM Workshop on Device-Free Human Sensing (DFHS ’20), November 15, 2020, Virtual Event, Japan.</p></blockquote><p> We are happily looking forward to pitching the concept of <b>NIAD</b> to the community!</p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-85131054071780357252020-10-13T05:30:00.002-07:002020-10-13T05:30:33.832-07:00Stop! Exploring Bayesian Surprise for Load Disaggregation<p>In our latest paper, which is the result of the ongoing collaboration between our lab and <a href="http://compsust.fas.sfu.ca/" target="_blank">SFU's Computational Sustainability Lab</a>, we bring the concept of Bayesian Surprise to NILM. When has enough prior training been done? When has a NILM algorithm encountered new, unseen data? We apply the notion of Bayesian surprise to answer these important questions for both, supervised and unsupervised algorithms.</p><blockquote><p><span face="Helvetica, Arial, Verdana, sans-serif" style="background-color: #eff9fe; caret-color: rgb(0, 0, 0); text-size-adjust: auto;">"Bayesian surprise quantifies how data affects natural or artificial observers, by measuring differences between posterior and prior beliefs of the observers" - <a href="http://ilab.usc.edu/surprise/">ilab.usc.edu</a></span></p></blockquote><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg2Mqi0By80VLONLj6di1JoRI8Ufuo9uIsvFSv8pvnNLB-f-J9Ovw_8QG0x7_VmSG-f6RBhJfz1Dxb7-Vqlqio1GDvg9bLAet9vgyWZdQF9wahu7xt1C4NgccQCSvBdb7cqb3k_iG20F753/s500/wow-1300922_1280.png" style="margin-left: auto; margin-right: auto;"><span style="color: black;"><img border="0" data-original-height="374" data-original-width="500" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg2Mqi0By80VLONLj6di1JoRI8Ufuo9uIsvFSv8pvnNLB-f-J9Ovw_8QG0x7_VmSG-f6RBhJfz1Dxb7-Vqlqio1GDvg9bLAet9vgyWZdQF9wahu7xt1C4NgccQCSvBdb7cqb3k_iG20F753/s320/wow-1300922_1280.png" width="320" /></span></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Bayesian Surprise is measured in "wow"<br /></td></tr></tbody></table>We compare the performance of several NILM algorithms to establish a suggested threshold on two combined measures of surprise: <u><i>postdictive surprise and transitional surprise</i></u>.<p></p><p>We provide preliminary insights and clear evidence showing a point of diminishing returns for model performance with respect to dataset size, which can have implications for future model development, dataset acquisition, as well as aiding in model flexibility during deployment.</p><p>The paper is to appear at the 5th International Workshop on Non-Intrusive Load Monitoring (NILM'20): </p><blockquote><p>Richard Jones, <u>Christoph Klemenjak</u>, Stephen Makonin, and Ivan V. Bajić. 2020. <a href="https://klemenjak.github.io/publication/2020-10-13-surprise" target="_blank">Stop! Exploring Bayesian Surprise to Better Train NILM</a>. In The 5th International Workshop on Non-Intrusive Load Monitoring (NILM ’20), November 18, 2020, Virtual Event, Japan.</p></blockquote><p>An author's copy can be obtained from <a href="https://klemenjak.github.io/publication/2020-10-13-surprise" target="_blank">Christoph's personal website</a></p><p>We are looking forward to discussing this novel approach for NILM.</p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-25936690108509468742020-08-03T02:25:00.002-07:002020-08-03T02:25:00.344-07:00SYND - A Synthetic Energy DatasetAs with related Machine Learning problems, applications like Non-Intrusive Load Monitoring (NILM) require a sufficient amount of data to train and validate new approaches. With SynD, we present a synthetic energy dataset emulating the power consumption of residential buildings. The dataset is freely available and contains 180 days of synthetic power data on aggregate level and individual appliances. <br /><br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEQP1gVbMugybtMF1goP3MvBgX7_likVBrBY3Kw4HY2YLM_p3Uf8y75zzdG8fu5AFWJeyZvrrokF0dvrOvXjlB8loTj-uqcIxW84TZAAa8lHWRQ-4MOkrI_9Qc1CnPg0rRSu2JS9UL-vtK/s816/real_devices.jpg" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="501" data-original-width="816" height="154" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEQP1gVbMugybtMF1goP3MvBgX7_likVBrBY3Kw4HY2YLM_p3Uf8y75zzdG8fu5AFWJeyZvrrokF0dvrOvXjlB8loTj-uqcIxW84TZAAa8lHWRQ-4MOkrI_9Qc1CnPg0rRSu2JS9UL-vtK/w250-h154/real_devices.jpg" title="The SynD dataset is based upon measurements of real devices" width="250" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The SynD dataset is based upon<br />measurements of real devices<br /></td></tr></tbody></table>SynD is the result of a custom simulation process that relies on power traces of real household appliances. During a measurement campaign in two Austrian households, we monitored 21 electrical household appliances. The main goal of the measurement campaign was to record representative power consumption patterns of those 21 appliances, where a each pattern is represented by the shape of the power consumption over time for a single operation.<br /><br /><div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTyxdhDRVcBa2iOMCdZSvPFj-SHUCHF-AOCA9DZBI-m8KHefaymZALJH2cH4I8h6NAxS21uU8zCm5Aj43TxNYZ9zD8uzl7GRma3ImUcqtsxZfROZkOtVmhtfaxrqZRZTC50x6-i0UoC99e/s1465/technical_evaluation.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1465" data-original-width="1300" height="410" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTyxdhDRVcBa2iOMCdZSvPFj-SHUCHF-AOCA9DZBI-m8KHefaymZALJH2cH4I8h6NAxS21uU8zCm5Aj43TxNYZ9zD8uzl7GRma3ImUcqtsxZfROZkOtVmhtfaxrqZRZTC50x6-i0UoC99e/w363-h410/technical_evaluation.png" width="363" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Technical validation of SYND by comparing with other datasets<br /></td></tr></tbody></table>In contrast to datasets entirely based on measurement campaigns, such as our<a href="https://arxiv.org/pdf/1405.3100v2.pdf"> dataset GREEND</a>, the SynD dataset is constructed from a simulation model utilizing the measured devices. This way, a synthetic but realistic power consumption dataset can be obtained. In a technical validation of the dataset we compared SynD with a number of measured datasets showing that SynD is well within the varaiation between mutual datasets.</div><div><br /></div><div>Wilfried Elmenreich states <b><i>“Usually I rely on measured data, but with the SYND dataset, we are among the first who created a convincing synthetic dataset.”</i></b></div><br />The full paper describing the dataset is available under an open access policy here:<br /><dl class="bib2xhtml"><dd>Christoph Klemenjak,
Christoph Kovatsch, Manuel Herold, and Wilfried Elmenreich.
<a href="https://www.nature.com/articles/s41597-020-0434-6.pdf">A synthetic
energy dataset for non-intrusive load monitoring in households</a>.
<cite>Scientific Data</cite>, 7(1):1–17, 2020.
(<a href="http://dx.doi.org/10.6084/m9.figshare.11940324">doi:10.6084/m9.figshare.11940324</a>)</dd></dl>The SynD dataset can be obtained freely at the <a href="https://github.com/klemenjak/synd/">SynD Github Repository</a>. <br />Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-78911496219343034832020-07-22T00:33:00.000-07:002020-07-22T00:33:11.408-07:00Adaptive Weighted Recurrence Graph blocks for event-based NILMTo this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load Monitoring (NILM) research, as researchers and practitioners are forced to invest a considerable amount of time in this task.<br />
<br />
This paper proposes adaptive weighted recurrence graph blocks (AWRG) for appliance feature representation in event-based NILM. An AWRG block can be combined with traditional deep neural network architectures such as Convolutional Neural Networks for appliance recognition. Our approach transforms one cycle per activation current into a weighted recurrence graph and treats the associated hyper-parameters as learn-able parameters.<br />
<br />
We evaluate our technique on two energy datasets, the industrial dataset LILACD and the residential PLAID dataset. The outcome of our experiments shows that transforming current waveforms into weighted recurrence graphs provides a better feature representation and thus, improved classification results. It is concluded that our approach can guarantee uniqueness of appliance features, leading to enhanced generalisation abilities when compared to the widely researched V-I image features. Furthermore, we show that the initialisation parameters of the AWRG’s have a significant impact on the performance and training convergence.<br />
<br />
We provide the implementation of <a href="https://github.com/sambaiga/AWRGNILM" target="_blank">AWRG on Github</a>. If you find this tool useful and use it (or parts of it), we ask you to cite the following work in your publications:<br />
<br />
<blockquote class="tr_bq">
A. Faustine, L. Pereira and C. Klemenjak, "<a href="https://ieeexplore.ieee.org/document/9144492" target="_blank">Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring</a>," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2020.3010621.</blockquote>
<br />
Learn more about the authors <a href="https://sambaiga.github.io/sambaiga/" target="_blank">Anthony Faustine</a>, <a href="https://www.alspereira.info/" target="_blank">Lucas Pereira</a> and <a href="https://klemenjak.github.io/" target="_blank">Christoph Klemenjak</a>.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-30503263292127823142020-07-09T08:19:00.002-07:002020-07-09T08:19:34.875-07:00Supporting the Grid with Dynamic Residential Load Scheduling Demand response (DR) for smart grids intends to balance the required power demand with the available supply resources. This is especially important with an increased amount of renewable energy sources since for most of them the energy yield cannot be shifted in time. For example, photovoltaic systems will provide their peak power at noon while customers might want to use energy at different times.<br />
<br />
Residential load scheduling systems provide a solution to this problem by incentivizing consumers to use energy at times where production is high while motivating lower energy consumption in times of peak power. The goals of such residential load scheduling systems are therefore manifold: to cut peak power, to follow supply and to reduce the overall energy cost for the customer.<br />
<br />
In a study we investigated different dynamic residential load scheduling systems with respect to optimal scheduling of household appliances on the basis of an adaptive consumption level pricing scheme (ACLPS). The proposed load scheduling system encourages customers to manage their energy consumption within the allowable consumption allowance of the proposed DR pricing scheme to achieve lower energy bills. <br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuHwn737i47BN2AvRFPPyW_LV5nTUFLmQJcfLIClqrUBPNqeP767WvA8YuP29pcJTx5GAnRcPoREcfFla1ugUxqPBkPxDvAPY3h4L4_WX6jVUU2I1KsuZ4wH5oiCfej6Wtsiir5_ErIsBy/s1600/Dynamic_Pricing.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1062" data-original-width="1327" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuHwn737i47BN2AvRFPPyW_LV5nTUFLmQJcfLIClqrUBPNqeP767WvA8YuP29pcJTx5GAnRcPoREcfFla1ugUxqPBkPxDvAPY3h4L4_WX6jVUU2I1KsuZ4wH5oiCfej6Wtsiir5_ErIsBy/s400/Dynamic_Pricing.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Dynamic pricing scheme motivating customers<br /> to avoid energy consumption in peak periods</td><td class="tr-caption" style="text-align: center;"><br /></td></tr>
</tbody></table>
Simulation results show that employing the proposed approach benefits the customers by reducing their energy bill and the utility companies by decreasing the peak load of the aggregated load demand. For a given case study, the proposed residential load scheduling system based on ACLPS allows customers to reduce their energy bills by up to 53% and to decrease the peak load by 35%.<br />
<br />
The full results are available in the paper<br />
<br />
H. T. Haider, O. H. See, and W. Elmenreich. <a href="http://mobile.aau.at/publications/haider-2016-dynamic_residential_load_scheduling.pdf">Dynamic residential load scheduling based on adaptive consumption level pricing scheme</a>. Electric Power Systems Research, 133:27–35, 2016.<br />
<div class="separator" style="clear: both; text-align: center;">
</div>
Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-20942117674877607022020-06-25T02:07:00.000-07:002020-06-25T02:07:37.897-07:00Investigating Synthetic Data for Load DisaggregationElectrical consumption data contain a wealth of information, and their collection at scale is facilitated by the deployment of smart meters. Data collected this way is an aggregation of the power demands of all appliances within a building, hence inferences on the operation of individual devices cannot be drawn directly. By using methods to disaggregate data collected from a single measurement location, however, appliance-level detail can often be reconstructed. A major impediment to the improvement of such disaggregation algorithms lies in the way they are evaluated so far: Their performance is generally assessed using a small number of publicly available electricity consumption data sets recorded from actual buildings. As a result, algorithm parameters are often tuned to produce optimal results for the used datasets, but do not necessarily generalize to different input data well.<br />
<br />
<blockquote class="tr_bq">
"We propose to break this tradition by presenting <a href="https://github.com/areinhardt/antgen" target="_blank">a toolchain to create synthetic benchmarking data sets</a> for the evaluation of disaggregation performance in this work. Generated synthetic data with a configurable amount of concurrent appliance activity is subsequently used to comparatively evaluate eight existing disaggregation algorithms." - Christoph Klemenjak</blockquote>
<br />
Instead of attempting to compile a benchmarking corpus from existing data sets, we present a methodological way to synthetically create data sets of definable disaggregation complexity. A high degree of realism can be accomplished by using accurate models of existing appliances and user activities. By forwarding synthetically generated data of gradually increasing levels of concurrent appliance activity to state-of-the-art disaggregation algorithms, we determine their sensitivity to specific data characteristics in a much more fine-grained way.<br />
<br />
We present a toolchain, ANTgen, that generates synthetic macroscopic load signatures for their use in conjunction with NILM (load disaggregation) tools. By default, it runs in scripted mode (i.e., with no graphical user interface) and processes an input configuration file into a set of CSV output files containing power consumption values and the timestamps of their occurrence, as well as a file summarizing the events that have occurred during the simulation). If you find this tool useful and use it (or parts of it), we ask you to cite the following work in your publications:<br />
<br />
<blockquote class="tr_bq">
Andreas Reinhardt and Christoph Klemenjak. 2020. <a href="https://www.areinhardt.de/publications/2020/Reinhardt_eEnergy_2020.pdf" target="_blank">How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study.</a> In Proceedings of the Eleventh ACM International Conference on Future Energy Systems (e-Energy ’20). Association for Computing Machinery, New York, NY, USA, 167–177. </blockquote>
<br />
Learn more about the authors <a href="https://www.areinhardt.de/" target="_blank">Andreas Reinhardt</a> and <a href="https://klemenjak.github.io/" target="_blank">Christoph Klemenjak</a>.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-28279296663447007022020-05-26T07:42:00.000-07:002020-05-26T07:42:13.710-07:00Augmenting an Assisted Living Lab with Non-Intrusive Load MonitoringThe global epidemic of the COVID-19 virus required severe restrictions
on travel and meetings. Among many other events, also the <a href="https://i2mtc2020.ieee-ims.org/">International Instrumentation and Measurement Technology Conference (I2MTC 2020)</a> could not take place physically.<br />
<br />
Therefore, we made our paper presentation in the form of a video:<br />
<br />
<center>
<iframe allowfullscreen="" frameborder="0" height="270" src="https://www.youtube.com/embed/YAcxwJa-HP8" width="480"></iframe></center>
<br />
In her talk, Hafsa Bousbiat describes how abnormal behavior can be detected among common household devices using Non-Intrusive Load Monitoring. The need for reducing our energy consumption footprint and the increasing number of electric devices in today’s homes is calling for new solutions that allow users to efficiently manage their energy consumption. Real-time feedback at device level would be of significant benefit for this application. In addition, the aging population and their wish to be more autonomous have motivated the use of this same real-time data to indirectly monitor the household’s occupants for their safety.<br />
By breaking down aggregate power consumption into appliance level consumption, Non-Intrusive Load Monitoring allows for reducing the energy consumption footprint and has the potential to indirectly monitor the elderly and help them to fulfil their wish to be more autonomous in a secure manner. Therefore, the work aims to depict an architecture supporting non-intrusive measurement with a smart electricity meter and the handling of these data using an open-source platform that allows us to visualize and process real-time data about the total consumed energy. The proposed architecture is depicted in the figure below.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj2C2g49ij5ajsPKsLIO5iZUuvrRgK72SQ3fV79egt5vm9fuxYzh1a1grruq3FP4SJCeu7FUHn-NYxMFZJEgt29022ue3uZzMZqt-D61-YV3gQjaMRusfGOTE6CjNNXaeFiHBpTuHI6brx/s1600/GlobalArchitect%25281%2529.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="360" data-original-width="842" height="207" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj2C2g49ij5ajsPKsLIO5iZUuvrRgK72SQ3fV79egt5vm9fuxYzh1a1grruq3FP4SJCeu7FUHn-NYxMFZJEgt29022ue3uZzMZqt-D61-YV3gQjaMRusfGOTE6CjNNXaeFiHBpTuHI6brx/s400/GlobalArchitect%25281%2529.png" width="485" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Proposed architecture for integrating an AAL with an energy monitoring system</td></tr>
</tbody></table>
<br />
<br />
More details about our work can be found in the full version of our paper <a href="https://arxiv.org/pdf/2002.05593" target="_blank">here</a>.<br />
<br />
<b>Please reference the paper as follows:</b><br />
<b> </b> <br />
<i>Hafsa Bousbiat, Christoph Klemenjak, Gerhard Leitner, and Wilfried Elmenreich. <a href="https://arxiv.org/pdf/2002.05593">Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring.</a> International Instrumentation and Measurement Technology Conference. May 2020. </i><br />
<br />
This work was supported by<b> </b><i>DECIDE - Doctoral school on "Decision-making in a digital environment"</i> at the University of Klagenfurt.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-65136631278109665612020-03-25T09:32:00.000-07:002020-03-25T09:32:04.278-07:00Reproducibility: Best practise examplesIn the blog article "<a href="https://demesos.blogspot.com/2020/03/why-it-is-important-to-share-your-code.html">Why it is important to share your code and make your paper accessible</a>", we advocated for sharing source code together with papers that involve a simulation-based evaluation. One argument is that such papers will be of higher utility to the reader and therefore, will create higher
impact.<br />
<br />
To help in this effort, the paper<br />
<br />
<i>Wilfried Elmenreich,
Philipp Moll, Sebastian Theuermann, and Mathias Lux.
<a href="https://peerj.com/articles/cs-240.pdf">Making simulation results
reproducible - Survey, guidelines, and examples based on Gradle and
Docker</a>.
<cite>PeerJ Computer Science</cite>, 5(e240):1–27, Dezember 2019.
(<a href="http://dx.doi.org/10.7717/peerj-cs.240">doi:10.7717/peerj-cs.240</a>) </i><br />
<br />
gives a good overview of which tools are suitable to provide your code in a useful way. It is also a paper that shares its code.<br />
<br />
If
you want to prepare datasets well, have a look at this paper, which
provides a useful set of checks to be considered for electricity
consumption datasets.<br />
<br />
<i>Christoph Klemenjak,
Andreas Reinhardt, Lucas Pereira, Mario Berges, Stephen Makonin, and Wilfried
Elmenreich.
<a href="http://makonin.com/doc/BuildSys_2019.pdf">Electricity consumption data
sets: Pitfalls and opportunities</a>.
In <cite>BuildSys ’19: The 6th ACM International Conference on Systems for
Energy-Efficient Buildings, Cities, and Transportation</cite>, pages 1–4.
ACM New York, November 2019.
(<a href="http://dx.doi.org/10.1145/3360322.3360867">doi:10.1145/3360322.3360867</a>)</i><br />
<br />
Last
but not least I would like to draw your attention to an excellent list
of "<a href="https://github.com/klemenjak/nilm-papers-with-code">Papers with Code</a>", compiled by researcher Christoph Klemenjak:<br />
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<a href="https://github.com/klemenjak/nilm-papers-with-code"><img alt="Papers with Code" border="0" data-original-height="272" data-original-width="839" height="128" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgVR7jMvU9MlQb5D8J5HqyqW61M36HhrX-mxjF-WvwDKGAIwaTt8TvbuZmZe6OzO8EuZRm94YBmoID5-XcgDlpnB-5g0tIcfX0f3JuPMHdjEN3fKnygFuEZ7TpGxOgxSmRaVu1-tJqM6PWp/s400/papers+with+code.png" width="400" /></a> </div>
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On
this page a list of papers targeting the field of load disaggregation
can be accessed. And the name of the page does not lie - all of
them come with code that can be freely accessed.</div>
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Happy researching! </div>
Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-13272940003483786482019-12-02T15:05:00.000-08:002019-12-02T15:05:00.965-08:00Privacy vs. NILM: Obfuscating your Power Consumption with Load Hiding<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUHSR8roSkkIHBxEGIMPiqZPanwee7mWwAsRqjA597zWFKu2Bf8QVtMbrjMoTGR50wXN3v1EX8HSdsejWcIuammbACaDof9_O8rdnO6SmF_sAgvTg_pf_yj7VzDTD3ZC8M6gb-7OEdtgWr/s1600/load-hiding.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="684" data-original-width="932" height="146" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUHSR8roSkkIHBxEGIMPiqZPanwee7mWwAsRqjA597zWFKu2Bf8QVtMbrjMoTGR50wXN3v1EX8HSdsejWcIuammbACaDof9_O8rdnO6SmF_sAgvTg_pf_yj7VzDTD3ZC8M6gb-7OEdtgWr/s200/load-hiding.png" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Load-based load hiding approach</td></tr>
</tbody></table>
With the development and introduction of smart metering, the energy information from costumers changes from infrequent manual meter readings to fine-grained energy consumption data. On the one hand, these measurements will lead to an improvement in costumers’ energy habits, but on the other hand, the fine-grained data produces information about a household and households’ inhabitants, which give rise to privacy issues because these monitoring results disclose user behavior which could be extracted by smart algorithms and techniques. The loss of privacy by load disaggregation and data mining is a huge upcoming smart grid and social issue which enforces the need for privacy-preserving techniques, which can be divided into the following three possibilities:<br />
<ol>
<li><i>Anonymization of metering data</i>: The metering data and customer identity are separated by a third-party id</li>
<li><i>Privacy-preserving metering data aggregation</i>: Metering data is geographically encapsulated by aggregating the metering data of co-located consumers </li>
<li><i>Masking and obfuscation of metering data</i>: Masking the power demand by adding or withdrawing the to the meter visible energy demand with the help of rechargeable batteries or controllable loads.</li>
</ol>
<br />In the paper<br /><br />D. Egarter, C. Prokop, and
W. Elmenreich.
<a href="http://arxiv.org/pdf/1406.2534v1">Load hiding of household's power
demand</a>.
In <cite>Proc. IEEE International Conference on Smart Grid Communications
(SmartGridComm'14)</cite>, Venice, Italy, 2014.<br /><br />a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented and compared. A load-based load hiding system controls appliances in a specific way to obfuscate a household’s power demand. For example, an electric water boiler could be instrumented to consume energy in a way that masks the power consumption of smaller household devices like coffee machines or a TV. There is no comfort loss expected for the customer: Overall, the boiler will consume a typical amount of energy and produce the expected amount of hot water.<br />
Using this approach, however, reduces the predictability of your energy consumption, which is good for privacy, but a disadvantage for grid operators. Wilhttp://www.blogger.com/profile/13527662530751362421noreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-22468054334822935012019-11-19T00:32:00.000-08:002019-11-19T00:32:14.940-08:00Position Paper on Energy Datasets @ ACM BuildSys Dear all,<br />
<br />
last week, Christoph Klemenjak presented our research paper on Energy Consumption Datasets at ACM BuildSys. The paper is the outcome of a collaboration with NILM experts and discusses pitfalls and opportunities with regard to future measurement campaigns.<br />
<br />
Abstract<br />
<br />
<i>Real-world data sets are crucial to develop and test signal processing and machine learning algorithms to solve energy-related problems. </i><br />
<i>Their scope and data resolution is, however, often limited to the means required to fulfill the experimenters' objectives and moreover governed by personal experience, budgetary and time constraints, and the availability of equipment.</i><br />
<i>As a result, numerous differences between data sets can be observed, e.g., regarding their sampling rates, the number of sensors deployed, their amplitude resolutions, storage formats, or the availability and extent of ground-truth annotations. </i><br />
<i>This heterogeneity poses a significant problem for researchers intending to comparatively use data sets because of the required data conversion, re-sampling, and adaptation steps.</i><br />
<i>In short, there is a lack of widely agreed best practices for designing, deploying, and operating electrical data collection systems.</i><br />
<i>We address this limitation by dissecting the collection methodologies used in existing data sets.</i><br />
<i>By offering recommendations for data collection, data storage, and data provision, we intend to foster the creation of data sets with increased usability and comparability, and thus a greater benefit to the community.</i><br />
<br />
Find the paper <a href="http://makonin.com/doc/BuildSys_2019.pdf" target="_blank">here</a>.<br />
<i><br /></i>
Please direct feedback to <a href="mailto:klemenjak@ieee.org">klemenjak@ieee.org</a><br />
<br />
Have a great day,<br />
<br />
ChristophUnknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-64338775367602488172019-11-19T00:26:00.001-08:002019-11-19T00:26:39.610-08:00New NILM paper on Comparability!<br />
<br />
Dear all,<br />
<br />
we proudly announce our latest NILM paper on comparability in NILM scholarship. In this paper, we discuss data noise, appliance events as well as performance evaluation in general. The paper is to be presented at 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) in Washington DC.<br />
<span style="background-color: white; color: #24292e; font-family: , , "segoe ui" , "helvetica" , "arial" , sans-serif , "apple color emoji" , "segoe ui emoji"; font-size: 16px;"><br /></span>
<u>Abstract</u><br />
<br />
<i>Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and generalisation abilities. Despite all progress made concerning disaggregation techniques, performance evaluation and comparability remains an open research question. The lack of standardisation and consensus on evaluation procedures makes reproducibility and comparability extremely difficult.</i><br />
<i>In this paper, we draw attention to comparability in NILM with a focus on highlighting the considerable differences amongst common energy datasets used to test the performance of algorithms. We divide discussion on comparability into data aspects, performance metrics, and give a close view on evaluation processes. Detailed information on pre-processing as well as data cleaning methods, the importance of unified performance reporting, and the need for complexity measures in load disaggregation are found to be the most urgent issues in NILM-related research. In addition, our evaluation suggests that datasets should be chosen carefully. We conclude by formulating suggestions for future work to enhance comparability.</i><br />
<br />
<br />
<a href="http://makonin.com/doc/ISGT-NA_2020b.pdf" target="_blank">Get the paper here.</a> - <a href="https://github.com/klemenjak/comparability" target="_blank">Explore supplemental material here.</a><br />
<br />
Please direct feedback and further comments to <a href="mailto:klemenjak@ieee.org">klemenjak@ieee.org</a><br />
<br />
Have a great day,<br />
<br />
ChristophUnknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-55401936187066327312019-03-14T11:53:00.000-07:002019-03-14T12:03:24.951-07:00Open thesis topics<div dir="ltr" style="text-align: left;" trbidi="on">
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<b><span lang="EN-IN" style="font-size: large; line-height: 17.12px;">Analysis of dataset for energy forecasting</span></b></h2>
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<b>Keywords: </b><span style="font-weight: normal;">RES, energy forecasting, training <gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525822255133641915889" style="text-align: left;"><gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525897912603741554037"><gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525900276729084563414">dataset</gwmw></gwmw></gwmw><span style="text-align: left;">, testing dataset, </span>prediction error</span></div>
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<b>Description and objectives:</b></h4>
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Integration of Renewable energy resources (RES) in today's power grid depends highly on the quality of energy forecasting outcome. However, the variability of RES poses several challenges to this integration. The idea of the proposed thesis is to assess the influence of different dataset composition which includes varying training, validation and testing dataset on the prediction <gwmw class="ginger-module-highlighter-mistake-type-2" id="gwmw-15525807215661188176367"><gwmw class="ginger-module-highlighter-mistake-type-2" id="gwmw-15525900272178136122234">error and to come</gwmw></gwmw> up with the most effective training method. The performance of the training method will be validated over publicly available dataset using available performance evaluation metrics.</div>
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<b>Main tasks:</b></h4>
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<li><span lang="EN-IN" style="text-indent: -0.25in;">The first task is to look for dataset having different characteristics in order to achieve better comparison and assessment of most effective approach when applied to different <gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525807215760218132153"><gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525896123420455768550"><gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525900272257022034096">datasets</gwmw></gwmw></gwmw>.</span></li>
<li>The next task is to train <gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525887509284663973686"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525896123477571181225"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525900272291956247287">neural network</gwmw></gwmw></gwmw> using machine learning algorithms for different dataset composition. </li>
</ul>
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<h4>
<b>Qualifications: </b><span style="font-weight: normal;">Programming</span> <span style="font-weight: normal;">skills in Python</span></h4>
<h4>
<b>Contact Details: </b><span lang="EN-IN">Ekanki SHARMA<gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525788793873383282580"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525896123786664511866"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525900272354681449102"> :</gwmw></gwmw></gwmw> </span><span lang="DE-AT"><a href="https://www.blogger.com/null"><span style="color: #101f69; font-family: "arial unicode ms" , "sans-serif"; font-size: 10pt; line-height: 14.2667px;">Ekanki.Sharma@aau.at</span></a></span><span class="apple-converted-space"><span lang="DE-AT" style="background: rgb(240 , 240 , 232); font-family: "arial unicode ms" , sans-serif; font-size: 10pt; line-height: 14.2667px;"><span style="text-align: start;"> </span></span></span></h4>
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<span class="apple-converted-space"><span lang="DE-AT" style="background: rgb(240 , 240 , 232); font-family: "arial unicode ms" , sans-serif; font-size: 10pt; line-height: 14.2667px;"><span style="text-align: start;"><br /></span></span></span></div>
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<b><span lang="EN-IN" style="line-height: 17.12px;"><span style="font-size: large;">Impact of data pre-processing techniques on forecasting accuracy</span><span style="font-size: 12pt;"><o:p></o:p></span></span></b><br />
<b><br /></b> <b>Keywords: </b><span style="text-align: left;">RES, data pre-processing, energy forecasting, feature selection, outlier rejection, forecasting accuracy</span></div>
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<b>Description and objectives:</b></h4>
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<span lang="EN-IN">To integrate renewable energy sources (RES) in <gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525889845136588944367"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525896123977135383662"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525900272466041426299">power grid</gwmw></gwmw></gwmw>, forecasting the photovoltaic (PV) yield is very important. Several techniques have been implemented in the literature which includes naive (time-series, statistical) methods <gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525891098562646169601"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525896124013524120652"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525900272514556989197">to</gwmw></gwmw></gwmw> soft computing techniques (Artificial neural network, </span><span lang="EN-IN" style="font-size: 12pt; line-height: 17.12px;">support vector machine, <gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525890733832653232758"><gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525896124016092918020"><gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525900272515498754411">grey</gwmw></gwmw></gwmw> prediction</span>) to improve the accuracy of the forecasting model. The idea of the thesis is to evaluate the impact of applying feature selection and <gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525891384390016024449"><gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525896124072623448342"><gwmw class="ginger-module-highlighter-mistake-type-1" id="gwmw-15525900272553115936991">outlier</gwmw></gwmw></gwmw> rejection techniques on forecasting accuracy. </div>
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<li><span lang="EN-IN" style="text-indent: -0.25in;">The first task is to search for available pre-processing techniques.</span></li>
<li><span lang="EN-IN" style="text-indent: -0.25in;">The next step is to compare the forecast accuracy with and without applying data pre-processing techniques.</span></li>
</ul>
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<b>Qualifications: </b>Programming skills in python</div>
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<span class="apple-converted-space"><span lang="DE-AT" style="background: rgb(240 , 240 , 232); font-family: "arial unicode ms" , sans-serif; font-size: 10pt; line-height: 14.2667px;"></span></span></div>
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<b>Contact Details: </b><span lang="EN-IN">Ekanki SHARMA<gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525788793935023933191"><gwmw class="ginger-module-highlighter-mistake-type-3" id="gwmw-15525900427794619596280"> :</gwmw></gwmw> </span><span lang="DE-AT"><a href="https://www.blogger.com/null"><span style="color: #101f69; font-family: "arial unicode ms" , "sans-serif"; font-size: 10pt; line-height: 14.2667px;">Ekanki.Sharma@aau.at</span></a></span><span class="apple-converted-space"><span lang="DE-AT" style="background: rgb(240 , 240 , 232); font-family: "arial unicode ms" , sans-serif; font-size: 10pt; line-height: 14.2667px;"><span style="text-align: start;"> </span></span></span></h4>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2054684438136045652.post-25653019789747403542018-03-01T02:24:00.000-08:002018-03-01T02:34:29.384-08:00Meetup on Future Research Challenges in Energy Informatics<h3>
18 - 19 April 2018 @ University of Klagenfurt, Austria</h3>
<div>
<br /></div>
<div>
<h4>
Objective</h4>
</div>
<div>
<div>
This two-day event aims to bring together researchers that are working on the topic of energy informatics in academia. </div>
<div>
<br /></div>
<div>
The focus of this meetup will be on <b>Non-Intrusive Load Monitoring (NILM)</b>. Other relevant subtopics of energy informatics such as data analytics, energy management systems, or artificial intelligence in Smart Microgrids are warmly welcome.</div>
<div>
<br /></div>
<div>
Within a small group, participants will present their latest findings, share experiences, discuss current issues, and discover possible ways of future cooperation and collaboration. </div>
</div>
<div>
<br /></div>
<div>
Researchers interested in joining are asked to apply with a talk title and abstract. Application deadline is <b>March 19th</b>. A committee will decide upon acceptance till <b>March 22nd</b>.</div>
<div>
<br /></div>
<div>
<a href="https://goo.gl/forms/Uj3RKtCLSxOwQgnF2" target="_blank">Apply now</a><br />
<br /></div>
<h4>
Remarks</h4>
<div>
<div>
The organisers would like to highlight that the University of Klagenfurt cannot provide any funding for expenses such as travelling or accommodation. Students of the University of Klagenfurt are eligible to attend the Keynote and Session 1.<br />
<br />
Find further information <a href="https://klemenjak.github.io/posts/2018/02/blog-post-2/" target="_blank">here</a>.</div>
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Unknownnoreply@blogger.com0Klagenfurt am Wörthersee, Österreich46.6364598 14.31222460000003646.4620323 13.989501100000036 46.8108873 14.634948100000036tag:blogger.com,1999:blog-2054684438136045652.post-49295965616270141472017-12-01T03:47:00.000-08:002017-12-06T06:41:01.526-08:00European Workshop on Non-Intrusive Load Monitoring (NILM)<div class="separator" style="clear: both; text-align: justify;">
From 6th to 7th of November, the 4th European Workshop on Non-Intrusive Load Monitoring (NILM) was held in London, United Kingdom. This event brought together researchers and professionals to present and discuss latest developments in the area of NILM as well as its applications. </div>
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The sessions comprised topics such as commercial & industrial NILM, innovative algorithms, deep learning approaches, and evaluation. Also, several vendors such as <a href="https://www.voltaware.com/" target="_blank">Voltaware</a>, <a href="http://qualisteo.com/ws/en/" target="_blank">Qualisteo</a> or <a href="https://verv.energy/" target="_blank">Verv</a> introduced their latest products.</div>
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In the poster & demo session, Christoph presented a poster on "Appliance Detection in Power Meter Readings". The poster illustrates how correlation can be utilised to detect electrical appliances in power readings. Especially for hardware with limited computational resources this approach shows promising results. For more information about the presented work refer to our <a href="http://mobile.nes.aau.at/publications/klemenjak-2017-Correlation_Filters.pdf">paper on correlation filters for appliance detection.</a></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRKixWNdwwnygUPUfDKD3jlO8gqIp9JTpvv1l-9SUxae1i-20Kzfw846dNQefaMKDKCuBte-SwX1AXgppqF7TYxeqYldE90nbL8hw440btFavGS-7WiUJoXBZA50VKewlXpxnQys8YaZhq/s1600/DN9xLBhWAAAl-Hy.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="900" data-original-width="1200" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRKixWNdwwnygUPUfDKD3jlO8gqIp9JTpvv1l-9SUxae1i-20Kzfw846dNQefaMKDKCuBte-SwX1AXgppqF7TYxeqYldE90nbL8hw440btFavGS-7WiUJoXBZA50VKewlXpxnQys8YaZhq/s400/DN9xLBhWAAAl-Hy.jpg" width="400" /></a></div>
<div style="text-align: center;">
<span style="font-size: x-small;">Picture: Christoph Klemenjak (on the left) presenting his poster on appliance detection</span></div>
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<span style="font-size: x-small;"><br /></span></div>
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A selection of the workshop's talks is available in form of a <a href="https://www.youtube.com/playlist?list=PLJrF-gxa0ImqaQswhzhRDFTMHnXRYfebi" target="_blank">Youtube playlist</a>.</div>
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The next major NILM event will be the <a href="http://nilmworkshop.org/2018/index.html" target="_blank">International Workshop on Non-Intrusive Load Monitoring</a>, which will take place in late February / early March 2018 in Austin, Texas.</div>
Unknownnoreply@blogger.com0London, Vereinigtes Königreich51.5073509 -0.1277582999999822351.1912379 -0.77320529999998222 51.8234639 0.51768870000001777