Dynamic adaptive event detection strategy based on power change-point weighting model

Gang Wang, Zhao Li, Zhao Luo, Tao Zhang, Mingliang Lin, Jiahao Li and Xin Shen

Applied Energy, 2024, vol. 361, issue C, No S0306261924002332

Abstract: Event detection is a prerequisite and key component of NILM (Non-Intrusive Load Monitoring) by monitoring transient changes in residential loads to discern whether a transient event has occurred in an appliance. However, the event detection performance of existing algorithms is affected by the operating environment, and it isn't easy to maintain high accuracy. For this reason, this paper proposes an adaptive event detection method based on the PCW (power change-point weights) model. Specifically, the DACUSUM (Dynamic Adaptive Cumulative Sum) algorithm with dynamic updating of parameters is first proposed, which effectively avoids the miss and false detection of CUSUM in the process of event detection. Secondly, the PCW model is proposed, which is capable of evaluating the effect of event detection of thresholds through the transient information entropy without prior knowledge. Lastly, based on the DACUSUM and PCW model, the threshold-adaptive event detection method is proposed, which takes the transient information entropy as the objective function and utilizes the genetic algorithm to dynamically adjust the thresholds to improve the performance of event detection under different operating environments. Taking eight typical appliances as an example, on the one hand, the proposed DACUSUM reduces the leakage and false detection phenomena compared with CUSUM and improves the event detection performance. On the other hand, the PCW model-based event detection strategy doesn't need human intervention or prior knowledge and is adaptable to different operating environments. The experimental results show that the proposed strategy achieves F1 scores of over 90% for the event detection of eight types of home appliances.

Keywords: NILM; Event detection; Adaptive thresholds; Change-point detection algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.122850

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