Toward smart energy user: Real time non-intrusive load monitoring with simultaneous switching operations

Yu Liu, Wei Liu, Yiwen Shen, Xin Zhao and Shan Gao

Applied Energy, 2021, vol. 287, issue C, No S0306261921001537

Abstract: Non-intrusive load monitoring is a promising technology in intelligent energy consumption management, which can provide insights for electricity use patterns and customer living habits, leading to the technical support of multiple smart energy use applications. As the most challenging problem in this specific field, the real time load disaggregation with simultaneous switching operations still remains unsolved due to its complexity and uncertainty. In this paper, a practical and feasible solution for the simultaneous action problems under real time non-intrusive load monitoring framework is proposed and investigated, which is capable of filling such research gap. At the first stage, to tackle the different kinds of overlapping combinations, an adaptive-window based detection approach is applied to capture the state changes. Secondly, a deep dictionary learning model is proposed in the real time load monitoring architecture, where the dictionary atom is generated in a semi-supervised modularization way and extended with high order dynamic features of appliances. Then, sparse coding formulized algorithm is presented to solve the problem, which is compatible of handling simultaneous occurrence of multiple switching events. The proposed model and algorithm are tested and verified in both simulation platform and field measurements. Results show that the operation-overlapping issue can be effectively addressed by the presented method, in addition to the high accuracy monitoring performance.

Keywords: Dynamic features; Machine learning; Real time load disaggregation; Simultaneous switching events; Sparse coding (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2021.116616

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