Development of Deep Generalized Radial Basis Function Network for Short-term Load Forecasting

Abstract

In this paper, an efficient method is proposed for short-term load forecasting (STLF) in power systems. The proposed method integrates Generalized Radial Basis Function Network (GRBFN) of Artificial Neural Network (ANN) with Autoencoder of pretraining in Deep Neural Network (DNN). GRBFN is an extension of Radial Basis Function Network (RBFN) in a way that the parameters of the Gaussian function are determined by the learning process. Autoencoder plays an important role to reduce the number of input variables, which means the dimensionality reduction due to the feature extraction. The hybrid model of GRBFN and Autoencoder results in DNN to improve forecasting model accuracy. Also, Evolutionary Particle Swarm Optimization (EPSO) of Evolutionary Computation is employed to optimize the parameters of GRBFN. The proposed method is successfully applied to real data of short-term load forecasting.