Research on Electricity Consumption Behavior of Users Based on Deep Learning

In order to better realize the classified management of the massive user electricity data and do a better job for peak shifting and averting of user side, this paper presents a hybrid model of deep belief network and extreme learning machine (DBN-ELM) for the analysis of electricity consumption behavior of users. Firstly, the total load curve is calculated. Then, the load curve of resident users is compared with the total load curve to make the classification rules of user electricity consumption behavior. Furthermore, the DBN-ELM hierarchy is optimized, and by traversing the number of hidden nodes, a reasonable number of hidden layer nodes are selected considering the simulation accuracy of different nodes. Finally, load data from Irish users are used as data sources, through comparative experiments with ELM and DBN networks, the results show that the proposed DBN-ELM algorithm for electricity consumption behavior analysis can extract high-level features better from the underlying features, and can better analyze the user's electrical consumption behavior, to improve the effect of peak shifting and averting work.