Adaptive threshold event detection method based on standard deviation

Abstract

Event detection is the foundation of event-based non-intrusive load detection solutions. Conventional event detection methods require a comprehensive consideration of the rated power levels of all devices within the detection scenario to set an appropriate threshold value. However, it cannot accurately detect both high- and low-power load events because of their fixed thresholds when loads with widely varying power change amplitudes are present simultaneously. Thus, in this study, an adaptive threshold event detection method based on standard deviation is proposed. First, the aggregated power data is intercepted by a sliding window for a short period of time, and the standard deviation is calculated for the aggregated power data within the window, and the event ends when the standard deviation reaches its maximum value. Then calculate the threshold for event detection based on the standard deviation, and then perform event detection based on the calculated threshold and then based on the bilateral sliding window cumulative sum (CUSUM) method. Finally, various load tests are performed with Electricity Consumption & Occupancy (ECO) data sets and private data sets , and the F1 values exceeded 90% in all three scenes: office, factory and laboratory, indicating that the proposed method in this study has high event detection performance.