Electricity Consumption Forecast Based on Empirical Mode Decomposition and Gated Recurrent Unit Hybrid Model

Electricity is one of the important needs in human production and life. The prediction of user power consumption can help power supply enterprises to analyze users' electricity consumption behavior, provide personalized services for users and formulate effective peak load shifting power supply scheme, which is very important for decision-making and demand response of power management side. As the daily electricity consumption data of users is nonlinear and nonstationary time series data, coupled with its susceptibility to climate change, social activities and other random factors, making electricity consumption forecast is a very challenging demand. At present, many deep learning models, such as recurrent neural network (RNN) and long short-term memory (LSTM) have been applied in electricity consumption forecasting and achieved good results. However, the direct use of these models cannot fully take into account the nonstationary characteristics of electricity data, and there is still room for improvement in the prediction effect. In this paper, a hybrid model of empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict user electricity consumption. First, the original nonstationary electricity consumption time series data is decomposed into multiple stationary component sequences through EMD, then each component is predicted through a multi-layer GRU network, and finally the prediction results of each component are combined to obtain the final Forecast results. Experimental results show that, compared with the direct use of LSTM, the proposed model can effectively reduce the error, achieve a better fitting effect, and improve the training efficiency to a certain extent.