Residential Appliance Detection Using Attention-based Deep Convolutional Neural Network

Improving energy efficiency management has become an important task for current electricity market participating entities, and monitoring consumption of pivotal appliances plays an important role in many applications. This paper focuses on detecting whether a residence possesses a certain type of appliance based on their electricity consumption and the problem of class imbalance within deep learning model training for large power appliances with the state ‘ON’. We propose a data-driven deep learning approach with attention mechanism to detect residential appliances from low-resolution aggregate energy consumption data. Firstly, the historical consumption profile of each user is divided into a specific length and labeled with the status of an appliance to generate training and test samples. Then, a deep convolutional neural network model with attention mechanism is trained, and the trained model is utilized to classify the test samples. Meanwhile, we obtain appliance status in a residence based on classification of multiple samples. Finally, we propose a novel approach of data generation for class imbalance of appliance detection using generative adversarial networks. In order to guarantee the quality, we devise a mechanism of self-validation to ensure generated data approximating real distribution of minor class samples. Experiments are conducted on a low-frequency smart meter data set sampled once every 30 minutes, and the results show that the proposed model performs better than hidden Markov model based algorithms and has good application prospects.