Enhanced RBF Neural Network Model for Time Series Prediction of Solar Cells Panel Depending on Climate Conditions (Temperature and Irradiance)

A radial basis function neural network is an effective technique for function approximation and prediction. It has been used in many applications in the real world; one of them is the time series prediction which is a relatively complex problem. In this paper, we propose an enhanced radial basis function neural network (RBFNN) model that depends on the standard RBF built-in MATLAB (newrb). The enhancement on newrb depends on the use of intelligent algorithms like K-means clustering, K-nearest neighbor, and singular value decomposition, to optimize the centers c, radii r, and weights w of the RBFNN. These algorithms replace the mathematical calculation used to find these parameters in newrb. The proposed enhanced model is applied to predict the solar cells energy production in Palestine using already installed solar panels in Jericho city. Solar irradiance and daily temperature are used as an input training data set for the proposed model, with the real output power of (2015) as the training supervisor. The model is applied to predict the output power within 1 month and for 1 year. Finally, a power output equation was optimized to calculate the solar energy depending on the irradiance and temperature with an acceptable accuracy. The experimental results show that the enhanced model performs more precisely than the traditional RBFNN and multilayer perceptron neural network methods, with low mean square error of relatively few neurons on the hidden layer.