Power Profile and Thresholding Assisted Multi-Label NILM Classification

Muhammad Asif Ali Rehmani, Saad Aslam, Shafiqur Rahman Tito, Snjezana Soltic, Pieter Nieuwoudt, Neel Pandey and Mollah Daud Ahmed
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Muhammad Asif Ali Rehmani: Department of Mechanical and Electrical Engineering, SF&AT, Massey University, Auckland 0632, New Zealand
Saad Aslam: School of Professional Engineering, Manukau Institute of Technology, Auckland 2104, New Zealand
Shafiqur Rahman Tito: School of Professional Engineering, Manukau Institute of Technology, Auckland 2104, New Zealand
Snjezana Soltic: School of Professional Engineering, Manukau Institute of Technology, Auckland 2104, New Zealand
Pieter Nieuwoudt: School of Professional Engineering, Manukau Institute of Technology, Auckland 2104, New Zealand
Neel Pandey: School of Professional Engineering, Manukau Institute of Technology, Auckland 2104, New Zealand
Mollah Daud Ahmed: Research Office, Manukau Institute of Technology, Auckland 2104, New Zealand

Energies, 2021, vol. 14, issue 22, 1-18

Abstract: Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the most cost-effective methods for load classification. The objective is to segregate the energy consumption of individual appliances from their aggregated energy consumption. The extracted energy consumption of individual devices can then be used to achieve demand-side management and energy saving through optimal load management strategies. Machine learning (ML) has been popularly used to solve many complex problems including NILM. With the availability of the energy consumption datasets, various ML algorithms have been effectively trained and tested. However, most of the current methodologies for NILM employ neural networks only for a limited operational output level of appliances and their combinations (i.e., only for a small number of classes). On the contrary, this work depicts a more practical scenario where over a hundred different combinations were considered and labelled for the training and testing of various machine learning algorithms. Moreover, two novel concepts—i.e., thresholding/occurrence per million (OPM) along with power windowing—were utilised, which significantly improved the performance of the trained algorithms. All the trained algorithms were thoroughly evaluated using various performance parameters. The results shown demonstrate the effectiveness of thresholding and OPM concepts in classifying concurrently operating appliances using ML.

Keywords: non-intrusive load monitoring (NILM); machine learning; smart building energy management systems (SBEM); multiclassification; computational complexity; energy efficiency; demand side management (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: 2021
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