Research on Forecast of Daily Electricity Consumption of Household Air Conditioning Based on Improved Long-short Memory Network

Abstract

Daily electricity consumption forecasting of home appliances can improve the accuracy and efficiency of the operation of home energy management systems. In this paper, an improved bidirectional long short memory network (BILSTM) model for predicting daily electricity consumption of household air conditioning is proposed. Firstly, and the “mutual information” is used to analyze the correlation between the daily electricity consumption of air conditioning and some environmental factors. Second, the environmental factors with strong correlation with the daily electricity consumption of air conditioning are selected as the influence factors, and these influence factors and the electricity data are taken as the characteristic input of the network. Finally, the improved bidirectional LSTM load prediction model which has been trained is used to forecast the daily electricity consumption of air conditioning. The experimental results show that the improved bidirectional LSTM network proposed in this paper can predict the daily electricity consumption of air conditioning in short term, and the maximum relative error of the predicted result is less than 5%.