How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study.

Published in ACM International Conference on Future Energy Systems (e-Energy ’20), 2020

Recommended citation: 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. https://www.areinhardt.de/publications/2020/Reinhardt_eEnergy_2020.pdf

Abstract:

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 data sets, 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. This way, we not only create a baseline for the comparison of newly developed disaggregation methods, but also point out the data characteristics that pose challenges for the state-of-the-art.

Index Terms—Machine Learning, Power and Energy, Smart Grid, Load Disaggregation, Synthetic Data

Recommended BibTex Citation:

@inproceedings{10.1145/3396851.3397691,
author = {Reinhardt, Andreas and Klemenjak, Christoph},
title = {How Does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study},
year = {2020},
isbn = {9781450380096},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3396851.3397691},
doi = {10.1145/3396851.3397691},
booktitle = {Proceedings of the Eleventh ACM International Conference on Future Energy Systems},
pages = {167–177},
numpages = {11},
keywords = {performance evaluation, NILM benchmarking, data set characteristics, load disaggregation, non-intrusive load monitoring, synthetic load signatures},
location = {Virtual Event, Australia},
series = {e-Energy ’20}
}

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