Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks

Inoussa Laouali, Isaías Gomes, Maria da Graça Ruano, Saad Dosse Bennani, Hakim El Fadili and Antonio Ruano ()
Additional contact information
Inoussa Laouali: DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
Isaías Gomes: DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
Maria da Graça Ruano: DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
Saad Dosse Bennani: SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez P.O. Box 2202, Morocco
Hakim El Fadili: LIPI, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Bensouda, Fez P.O. Box 5206, Morocco
Antonio Ruano: DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal

Energies, 2022, vol. 15, issue 23, 1-29

Abstract: Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.

Keywords: non-intrusive load monitoring (NILM); energy disaggregation; neural networks; multi-objective genetic algorithm; low frequency power data; convex hull algorithms (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 (2)

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