Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation

• URL: http://arxiv.org/abs/2001.07708v1
• Date: Mon, 20 Jan 2020 10:13:51 GMT
• Title: Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation
• Authors: Christoph Klemenjak, Stephen Makonin and Wilfried Elmenreich
• Abstract summary: Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Despite progress made concerning disaggregation techniques, performance evaluation and comparability remains an open research question. 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.
• Score: 1.0312968200748116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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