An artificial immune-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, the Artificial Immune System (AIS) was selected as the optimizer for the training process of the Multi-Layer Feedforward Neural Network (MLFNN). AIS was used to optimize 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 kWh energy as its output. When compared with the classically trained MLFNN, the proposed HMLFNN was found to be superior in terms of having shortest computation time and lower prediction error.