A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation

Abstract

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, conventional machine learning, and deep learning. The rule-based approach entails hand-crafted feature engineering and carefully calibrated thresholds; the accuracies of statistics-based and conventional 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 and 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 backpropagation (RSB) algorithm for weight filters is proposed to improve the robustness of the standard wide-deep model. Compared to the standard wide-deep, pure CNN, and SMR models, the hybrid wide-deep model powered by RSB demonstrates its superiority in terms of accuracy, convergence speed, and robustness.

A Non-Intrusive Residential Electric Bicycle Load Monitoring Method Based on Hybrid Feature Bitmap and DeiT

“…The second step is normalization. Changing the values of current and voltage expended sequence per unit which ranges from 0 to 1 is necessary by formula (Ruano et al, 2019) and (Li et al, 2021).…”

“…In the formula given by Ruano et al (2019) and Li et al (2021), i′ k and i k are the k th points in the current sequence before and after normalizing, respectively. I max and I min is the maximum and minimum value of the current sequence before normalizing, respectively.…”

“…Then, the load event should be detected for gaining the start and end time. Li proposed an algorithm for load event detection in smart homes based on wide and deep learning that combines the convolutional neural network and the soft-max regression (Li et al, 2021). As for data collection and event detection, electric bicycles follow many commercial plug-in devices and detectors of NILM.…”

Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine

“…Feature extraction refers to the use of digital signal processing technology or circuit analysis theory to extract valuable indicators from the collected electrical signals to distinguish different types of electrical equipment. In noninvasive load identification tasks, common load characteristics include voltage and current waveform, current harmonics (Cui et al, 2020), active and reactive power, V-I trajectory (Liu et al, 2021;Xiang et al, 2022), instantaneous power (Li et al, 2021), etc. Among them, V-I trajectory features are characterized by high repeatability and strong stability (Gao et al, 2016;Wu et al, 2020).…”