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.

Investigating the Benefit of Time-Series Imaging for Load Disaggregation

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: an imaging block.  

A Recurrence Plot

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. 

The main contribution presented in this paper is a comparison study of three common imaging techniques: 

• Gramian Angular Fields, 
• Markov Transition Fields, 
• Recurrence Plots.

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. ´

The outcome of our study reveals that in 19 of 24 cases, 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. 

Our paper is to appear at the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’20):

Hafsa Bousbiat, Christoph Klemenjak, and Wilfried Elmenreich. 2020. Exploring Time Series Imaging for Load Disaggregation. In The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’20), November 18–20, 2020, Virtual Event, Japan.

 We are looking forward to discussing our paper at BuildSys!

Evolving NILM to NIAD: Non-Intrusive Activity Detection

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. 

In collaboration with Andreas Reinhardt 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 Non-Intrusive Activity Detection (NIAD), 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.

Our paper is to appear at the 2nd ACM Workshop on Device-Free Human Sensing (DFHS'20):

Andreas Reinhardt and Christoph Klemenjak. 2020. Device-Free User Activity Detection using Non-Intrusive Load Monitoring: A Case Study. In The 2nd ACM Workshop on Device-Free Human Sensing (DFHS ’20), November 15, 2020, Virtual Event, Japan.

 We are happily looking forward to pitching the concept of NIAD to the community!

SYND - A Synthetic Energy Dataset

As 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.

The SynD dataset is based upon
measurements of real devices

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.

Technical validation of SYND by comparing with other datasets

In contrast to datasets entirely based on measurement campaigns, such as our dataset GREEND, 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.

Wilfried Elmenreich states “Usually I rely on measured data, but with the SYND dataset, we are among the first who created a convincing synthetic dataset.”

The full paper describing the dataset is available under an open access policy here:

Christoph Klemenjak, Christoph Kovatsch, Manuel Herold, and Wilfried Elmenreich. A synthetic energy dataset for non-intrusive load monitoring in households. Scientific Data, 7(1):1–17, 2020. (doi:10.6084/m9.figshare.11940324)

The SynD dataset can be obtained freely at the SynD Github Repository.

Investigating Synthetic Data for Load Disaggregation

Electrical 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.

 "We propose to break this tradition by presenting a toolchain to create synthetic benchmarking data sets 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

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.

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:

Andreas Reinhardt and Christoph Klemenjak. 2020. How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study. 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. 

Learn more about the authors Andreas Reinhardt and Christoph Klemenjak.

Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring

The global epidemic of the COVID-19 virus required severe restrictions on travel and meetings. Among many other events, also the International Instrumentation and Measurement Technology Conference (I2MTC 2020) could not take place physically.

Therefore, we made our paper presentation in the form of a video:


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.
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.

Proposed architecture for integrating an AAL with an energy monitoring system

More details about our work can be found in the full version of our paper here.

Please reference the paper as follows:
 
Hafsa Bousbiat, Christoph Klemenjak, Gerhard Leitner, and Wilfried Elmenreich. Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring. International Instrumentation and Measurement Technology Conference. May 2020.

This work was supported by DECIDE - Doctoral school on "Decision-making in a digital environment" at the University of Klagenfurt.

Reproducibility: Best practise examples

In the blog article "Why it is important to share your code and make your paper accessible", 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.

To help in this effort, the paper

Wilfried Elmenreich, Philipp Moll, Sebastian Theuermann, and Mathias Lux. Making simulation results reproducible - Survey, guidelines, and examples based on Gradle and Docker. PeerJ Computer Science, 5(e240):1–27, Dezember 2019. (doi:10.7717/peerj-cs.240) 

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.

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.

Christoph Klemenjak, Andreas Reinhardt, Lucas Pereira, Mario Berges, Stephen Makonin, and Wilfried Elmenreich. Electricity consumption data sets: Pitfalls and opportunities. In BuildSys ’19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pages 1–4. ACM New York, November 2019. (doi:10.1145/3360322.3360867)

Last but not least I would like to draw your attention to an excellent list of "Papers with Code", compiled by researcher Christoph Klemenjak:

 

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.

Happy researching!

Position Paper on Energy Datasets @ ACM BuildSys

Dear all,

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.

Abstract

Real-world data sets are crucial to develop and test signal processing and machine learning algorithms to solve energy-related problems. 
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.
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. 
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.
In short, there is a lack of widely agreed best practices for designing, deploying, and operating electrical data collection systems.
We address this limitation by dissecting the collection methodologies used in existing data sets.
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.

Find the paper here.

Please direct feedback to klemenjak@ieee.org

Have a great day,

Christoph

New NILM paper on Comparability!

Dear all,

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.

Abstract

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.
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.


Get the paper here.   -   Explore supplemental material here.

Please direct feedback and further comments to klemenjak@ieee.org

Have a great day,

Christoph

Meetup on Future Research Challenges in Energy Informatics

18 - 19 April 2018 @ University of Klagenfurt, Austria

Objective

This two-day event aims to bring together researchers that are working on the topic of energy informatics in academia. 

The focus of this meetup will be on Non-Intrusive Load Monitoring (NILM). Other relevant subtopics of energy informatics such as data analytics, energy management systems, or artificial intelligence in Smart Microgrids are warmly welcome.

Within a small group, participants will present their latest findings, share experiences, discuss current issues, and discover possible ways of future cooperation and collaboration. 

Researchers interested in joining are asked to apply with a talk title and abstract. Application deadline is March 19th. A committee will decide upon acceptance till March 22nd.

Apply now

Remarks

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.

Find further information here.

European Workshop on Non-Intrusive Load Monitoring (NILM)

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. 

The sessions comprised topics such as commercial & industrial NILM, innovative algorithms, deep learning approaches, and evaluation. Also, several vendors such as Voltaware,  Qualisteo or Verv introduced their latest products.

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 paper on correlation filters for appliance detection.

Picture: Christoph Klemenjak (on the left) presenting his poster on appliance detection

A selection of the workshop's talks is available in form of a Youtube playlist.

The next major NILM event will be the International Workshop on Non-Intrusive Load Monitoring, which will take place in late February / early March 2018 in Austin, Texas.

Energy Informatics in Klagenfurt at a Glance

In the present day, the electric power grid faces an evolutionary step towards the smart grid. The smart grid is defined as the enhancement of the electric power grid with information and communication technology. This sort of digitisation will enable a bidirectional flow of energy and information within the power grid and provide several novel applications and allow to unlock the full potential of renewable energy technologies. To cope with the challenge of digitisation in power grids, key elements of future energy systems have to be explored and furthermore, computational methods have to be developed and refined.

The Smart Grids group, located at the University of Klagenfurt, contributes to this challenge by investigating how power meter readings can be analysed to discover solutions to sustainably increase the energy efficiency of energy systems. 

"We carried out a measurement campaign in eight selected households to track power consumption of individual electrical appliances for over one year. The main outcome was the GREEND dataset, which was analysed to gain further insights into energy consumption behaviour", says Andrea Monacchi, the coordinator of the campaign. 

Figure: Overview of the households and appliances

On basis of such data, policies can be formulated to improve energy efficiency by shedding of standby losses, postponement to off-peak periods, replacement of inefficient appliances, and operation curtailment. The networked power meter (smart meter) represents the key element in the transition towards the smart grid since such measurement equipment provides feedback to the users, other appliances, and the electric utility. 

"The smart meter is a vital tool for researchers to record the energy consumption of households and industrial buildings. On basis of the collected data, computational methods and effectivity of efficiency measures are evaluated", states Christoph Klemenjak. "We developed an open-hardware smart metering board called YoMo, which is an extension unit for the Arduino platform. The YoMo is designed to monitor current flow and voltage level, as well as active, reactive, and apparent power at the feed point of households."

Figure: The YoMo smart metering board

In general, smart meters serve several purposes such as billing of consumed energy, providing immediate feedback to the users, or switching loads. Smart metering and fine-grained energy data are one of the major enablers for the future smart grid and an improved energy efficiency in smart homes. On the one hand these fine-grained measurements will lead to improved energy consumption habits, on the other hand the fine-grained data produces many questions with respect to privacy issues. To ensure both, household privacy and smart meter information, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. 

"The load hiding technique we developed works with devices that are already in the system. By controlling these household appliances in a certain way, your power profile becomes scrambled", Professor Elmenreich explains. 

Selected Publications 

C. Klemenjak, D. Egarter, and W. Elmenreich. YOMO-The Arduino based smart metering board. Computer Science - Research and Development (Springer), 2016. 

M. Pöchacker, D. Egarter, and W. Elmenreich. Proficiency of power values for load disaggregation. IEEE Transactions on Instrumentation and Measurement, 2016. 

D. Egarter, V. P. Bhuvana, and W. Elmenreich. PALDi: Online Load Disaggregation based on Particle Filtering. IEEE Transactions on Instrumentations and Measurement, 2015. 

D. Egarter, C. Prokop, and W. Elmenreich. Load Hiding of Household's Power Demand. IEEE International Conference on Smart Grid Communications, 2014. 

A. Monacchi, D. Egarter, W. Elmenreich, S. D'Alessandro, and A. M. Tonello. GREEND: An energy consumption dataset of households in Italy and Austria. In Proc. IEEE International Conference on Smart Grid Communications, 2014. 

The Energy Informatics Lab

Data will be one of the most important resources in the future. Among other timely research questions, activities in energy informatics explore how data provided by advanced metering infrastructure (AMI) can be utilised in the most adequate and efficient way. In order to record energy data, the Energy Informatics Lab provides a wide range of measurement instruments such as smart meters, oscilloscopes, and self-designed power meters. Furthermore, modern computer infrastructure allows to evaluate novel computational methods for prediction and user feedback for future energy systems.

Figure: A small selection of the present measurement equipment

The Energy Informatics lab also integrates a well-equipped soldering work station and a 3D printer, which allows the researchers to craft prototypes and custom enclosures for all kinds of measurement equipment. “Previously our students often had problems apply the theoretic concepts they learned. The Energy Informatics Lab offers the possibility to try out things in practice, which gives them a different perspective,” states Professor Wilfried Elmenreich.

Figure: The present 3D printer allows rapid prototyping

More detailed information can be found in our research blogs Energy Informatics, The Smart Grid, and on the institute's web page Networked and Embedded Systems.