Short-term air conditioning cooling load prediction based on long-short-term memory network

This paper proposes a short-term air conditioning cooling load prediction model based on the long-short-term memory network (LSTM), which only uses historical load data to predict hourly cooling load in the next day. By comparing with the back-propagation neural network (BPNN) model, the accuracy of the model is testified. In order to further improve the prediction accuracy of the model, this study optimizes the network structure including the number of neurons in input layer, output layer and hidden layer and prediction strategies to obtain the optimal prediction model. The results show that the prediction model based on the LSTM can forecast cooling load accurately, and perform better than BPNN model, and the root mean squared error and its coefficient of variation reduce 116 kW and 5.42%, respectively. The optimized results show that the best combination of input and output is to use historical seven-day load data to predict the hourly air conditioning load of the next day. When the number of neurons in the hidden layer is 60, the prediction accuracy of the model is higher and more stable. The stepwise output prediction strategy can reduce the prediction error at the peak load and is helpful to improve the prediction accuracy.