Forecasting energy generation in large photovoltaic plants using radial belief neural network

Abstract

Forecasting the energy generation from the solar power is considered challenging due to inaccuracies in forecasting, reliability issues and substantial economic losses in power systems. Hence, it is necessary to consider wide features from the solar power generation point of view. In this paper, the study uses large features set to feed the deep learning classifier for optimal prediction of energy generation from the photovoltaic (PV) plants. The features selection and prediction modules automates the process of optimal prediction of energy using Radial Belief Neural Network (RBNN). The Restricted Boltzmann Machines (RBM) is used for rule set generation based on the feature extracted and the rule set generation is powered by action-reward based Reinforcement Learning (RL) method. The experiments are conducted with rich set of input features on large PV plants that ranges between 1, 50, 100 and 1000. The performance of the proposed model is compared with various metrics that includes: Root mean squared error (RMSE), normalized root mean squared error (NRMSE), mean bias error (MBE), Mean absolute error (MAE), Maximum absolute error (MaxAE), mean absolute percentage error (MAPE), Kolmogorov–Smirnov test integral (KSI) and OVER metrics, Skewness and kurtosis and variability estimation metrics. The simulation results show that the RBNN offers improved prediction ability with reduced errors than other deep and machine learning classifiers.