A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation - Li, Chen; Liang, Gaoqi; Zhao, Huan; Chen, Guo (Frontiers in Energy Research, 2021)
Event detection is an important application in demand-side management. Precise event detection algorithms can improve the accuracy of non-intrusive load monitoring (NILM) and energy disaggregation models. Existing event detection algorithms can be divided into four categories: rule-based, statistics-based, conven-tional machine learning, and deep learning. The rule-based ap-proach entails hand-crafted feature engineering and carefully calibrated thresholds; the accuracies of statistics-based and con-ventional machine learning methods are inferior to the deep learning algorithms due to their limited ability to extract complex features. Deep learning models require a long training time and are hard to interpret. This paper proposes a novel algorithm for load event detection in smart homes based on wide & deep learning that combines the convolutional neural network (CNN) and the soft-max regression (SMR). The deep model extracts the power time series patterns and the wide model utilizes the percentile information of the power time series. A randomized sparse back-propagation (RSB) algorithm for weight filters is proposed to improve the robustness of the standard wide-deep model. Com-pared to the standard wide-deep, pure CNN, and SMR models, the hybrid wide-deep model powered by RSB demonstrates its supe-riority in terms of accuracy, convergence speed, and robustness.