Fast Evolutionary Programming-based Hybrid Multi-Layer Feedforward Neural Network for predicting Grid-Connected Photovoltaic system output

Abstract

This paper presents a Hybrid Multi-Layer Feedforward Neural Network (HMLFNN) technique for predicting the output from a Grid-Connected Photovoltaic (GCPV) system. In the proposed HMLFNN, Fast Evolutionary Programming (FEP) was employed to optimize the training process of the Multi-Layer Feedforward Neural Network (MLFNN). FEP was used to select the optimal values for the number of neurons in the hidden layer, the learning rate, the momentum rate, the type of activation function and the learning algorithm. In addition, the MLFNN utilized solar irradiance (SI) and module temperature (MT) as its inputs and AC kWh energy as its output. When compared with the Classical Evolutionary Programming (CEP) trained MLFNN, the proposed FEP-based HMLFNN offered superior performance by producing lower computation time and lower prediction error.