Application of Gray Wolf Optimization Algorithm in Urban Electricity Load Forecasting Model

Abstract

A combined prediction model based on long short-term memory neural network (LSTM) and convolutional neural network (CNN) is proposed in order to increase the prediction accuracy of short-term load. To address the issue that the gray wolf optimization (GWO) search process is prone to falling into local optimum. An improved grey wolf algorithm (IGWO) is proposed to update the convergence factor using the lower incomplete gamma function to improve the global optimization performance. The Dropout technique is used to improve the generalization ability of the model; the network layers are improved by increasing the initialization of weights; the model is trained using an adaptive moment estimation (Adam) optimizer, test data are input to the trained neural network model, and finally, the optimized model is used for prediction. The high prediction accuracy of the proposed method is demonstrated experimentally.