Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model

Ce Peng, Guoying Lin, Shaopeng Zhai, Yi Ding and Guangyu He
Additional contact information
Ce Peng: Marketing Department of Guangdong Power Grid Company, Guangzhou 510000, China
Guoying Lin: College of Electrical Engineering, Zhejiang University, Hangzhou 310000, China
Shaopeng Zhai: The Ministry of Education Key Laboratory of Control of Power Transmission and Conversion, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200200, China
Yi Ding: College of Electrical Engineering, Zhejiang University, Hangzhou 310000, China
Guangyu He: The Ministry of Education Key Laboratory of Control of Power Transmission and Conversion, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200200, China

Energies, 2020, vol. 13, issue 21, 1-19

Abstract: Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.

Keywords: NILM; deep learning; deep user model; deep appliance group model; user behavior (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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