Benchmarking Optimization-Based Energy Disaggregation Algorithms

Oladayo S. Ajani, Abhishek Kumar, Rammohan Mallipeddi, Swagatam Das and Ponnuthurai Nagaratnam Suganthan
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Oladayo S. Ajani: Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Korea
Abhishek Kumar: Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Korea
Rammohan Mallipeddi: Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Korea
Swagatam Das: Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India
Ponnuthurai Nagaratnam Suganthan: School of Electrical and Electronic Engineering, Nanyang Technical University, Singapore 639798, Singapore

Energies, 2022, vol. 15, issue 5, 1-19

Abstract: Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED.

Keywords: non-intrusive load monitoring; optimization-based energy disaggregation; benchmarking; evaluation metrics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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