On the Potential of Self-Organizing Energy Systems

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.

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.

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.

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.

Paper

Kristina Wogatai. Exploring the Potential ofSelf-Organizing Applications in Energy Networks. In Proc. IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), Toronto, Canada, September 25-29, 2023.

Energy Disaggregation with NILM on a Raspberry Pi with Smart-Metering Extension

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.

Smart Metering Extension for Raspberry Pi

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.
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.
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.

Color indicating detected devices by the NILM algorithm

 To learn more, check out the paper

Johannes Winkler, Hafsa Bousbiat, Stefan Jost, and Wilfried Elmenreich. Energy Disaggregation with NILM on a Raspberry Pi with Smart-Metering Extension. In Proc. 2023 2nd International Conference on Power Systems and Electrical Technology (PSET 2023), Milan, Italy, August 25-27, 2023.

or visit our NILM Raspberry Pi project on Github.

Unlocking the Full Potential of Neural NILM: On Automation, Hyperparameters & Modular Pipelines

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.

Overview of the NILM pipeline in Deep-NILMTK

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.

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.

Leveraging this concept and DL best practices, a case-study of creating an online NILM benchmark repository is provided at https://github.com/BHafsa/DNN-NILM-benchmark considering eight of the most popular deep NILM algorithms. All sources relative to the tool are publicly available on Github https://github.com/BHafsa/deep-nilmtk-v1 along with the corresponding documentation.

Further information can be found in the paper

Hafsa Bousbiat, Anthony Faustine, Christoph Klemenjak, Lucas Pereira, and Wilfried Elmenreich. Unlocking the full potential of neural NILM: On automation, hyperparameters & modular pipelines. IEEE Transactions on Industrial Informatics, pages 1–9, 9 2022. (doi:10.1109/TII.2022.3206322)

 

A New Unobtrusive Activity Monitoring Framework to Age Safely in the Digital Era

In “Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances”,  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.

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 Institute of Networked and Embedded Systems has a long-standing experience in developing and using NILM technologies, with their work spanning from fundamental research to practical applications of the technology.

The research work is a collaboration between three institutes  (Digital Age Research Center (D!ARC), Institute of Networked and Embedded Systems (NES), and the Department of Information Systems (ISYS)) at the University of Klagenfurt and is part of the dissertation project of Hafsa Bousbiat, a promising young female researcher who is part of the DECIDE doctoral school. 

Overview of the proposed activity monitoring framework

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.

Further information can be found in the paper:

H. Bousbiat, G. Leitner, and W. Elmenreich. Ageing safely in the digital era: A new unobtrusive activity monitoring framework leveraging on daily interactions with hand-operated appliances. Sensors, 22(4), 2022. (doi:10.3390/s22041322)

Neural NILM Learning Paradigms: From Centralised to Decentralised Learning

Centralised vs collaborative learning

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.

Further information can be found in the paper:

Hafsa Bousbiat, Christoph Klemenjak, Yassine Himeur, Wilfried Elmenreich, Abbes Amira, Wathiq Mansoor, and Shadi Atalla. Neural NILM learning paradigms: From centralised to decentralised learning. In Proceedings of the 2022 5th International Conference on Signal Processing and Information Security (ICSPIS), pages 138–142, December 2022. (doi:10.1109/icspis57063.2022.10002485)

The paper also won the best paper award at the 5th International Conference on Signal Processing and Information Security (ICSPIS) in December 2012.

The Role of Renewable Energies in the Arctic

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. 

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.

Energy Informatics 2023 in Vienna -- Call for Papers

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.

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+).

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:

• ICT for future energy systems, sector coupling and the integration of intermittent renewable generation
• Information and decision support systems for future energy markets and mechanisms
• Energy system modelling and (open) energy system data
• Protocols and architectures for IT systems in the energy sector
• Data analytics and machine learning for smart energy systems and decentralised decision-making, as well as platforms for data analysis
• Open data and software for energy research
• Management of distributed generation and demand side management
• ICT for (multi-) energy networks and micro-grids
• Energy-efficient mobility, charging management for electric vehicles, energy-aware traffic control, and smart grid integration of mobile storage
• Smart buildings, digital metering, occupant comfort, and user interaction
• Adoption of ICT in the energy sector
• Cross-cutting issues including cyber security and privacy protection, interoperability, verification of networked smart grid systems

Posters, Demos and Workshops

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.

Submission and Publication

Submitted papers will be reviewed in a double-blind process. Accepted and presented papers will be published in the Springer Open Journal Energy Informatics (https://energyinformatics.springeropen.com). 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 http://www.energy-informatics.eu/. 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.

Important dates

Apr 09, 2023:                     Submission of papers

May 16, 2023:                     Decision acceptance (assignment of shepherds) / rejection

May – Aug                          Incremental revision process between author and shepherd

Jul 02, 2023:                        Camera-ready deadline for poster abstracts for accepted contributions

Oct 04 2023:                      14th Doctoral Workshop Energy Informatics

Oct 05-06, 2023:                12th DACH+ Conference on Energy Informatics