Will NILM Technology Replace Multi-Meter Telemetry Systems for Monitoring Electricity Consumption?

Bartłomiej Gawin (), Robert Małkowski and Robert Rink
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Bartłomiej Gawin: Department of Business Informatics, Faculty of Management, University of Gdańsk, 81-864 Sopot, Poland
Robert Małkowski: Department of Power Electronics and Electrical Machines, Faculty of Electrical and Control Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland
Robert Rink: Automatics and System Analysis Department, Gdańsk Division, Institute of Power Engineering Research Institute in Warsaw, 01-330 Warsaw, Poland

Energies, 2023, vol. 16, issue 5, 1-26

Abstract: The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energy consumption controls and planning. Non-intrusive load monitoring (NILM) is the analytical method used to monitor the energy and disaggregate total electrical usage into appliance-related signals as an alternative to installing multiple electricity meters in the building. However, despite considerable progress, there are a limited number of tools dedicated to the problem of reliable and complete energy disaggregation. This paper presents an experiment consisting in designing an electrical system with electrical energy receivers, and then starting NILM disaggregation using machine learning algorithms (MLA). The quality of this disaggregation was assessed using dedicated indicators. Subsequently, the quality of these MLA was also verified using the available BLUED data source. The results show that the proposed method guarantees non-intrusive load disaggregation but still requires further research and testing. Measurement data have been published as open research data and listed in the literature section repository.

Keywords: NILM; MLA; energy efficiency management; energy disaggregation (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: 2023
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