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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 hold a certain type appliance based on their electricity consumption. 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; Finally, we obtain appliance status in a residence based on classification of multiple samples. Experiments are conducted on a low-frequency smart meter data set sampled once every 30 minutes, whose results show our proposed model performs better than hidden Markov model based algorithms and has good application prospects.

Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.