Day-ahead prediction of solar power output for grid-connected solar photovoltaic installations using Artificial Neural Networks

Abstract

Solar Photovoltaic (PV) systems are gaining popularity as a form of alternative energy with increased environmental awareness, renewable energy usage and concern for energy security. Lack of area-specific forecasts for the power output of grid-connected photovoltaic system hinders tapping solar power on a large scale. The objective of this paper is to estimate the profile of produced power of a grid-connected 20 kWp solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India [10° 44' 42.3816" N, 78° 47' 9.4524" E]. An Artificial Neural Network (ANN)-based model is proposed in this paper. An experimental database of solar power output (from 7th January 2014 to 10th February 2014) has been used for training the ANN. Simulations were carried out with the Neural Network Fitting Toolbox of MATLAB software. Day-Ahead Forecasting results indicate that the proposed model performs well with great accuracy and efficiency. Statistical error analysis in terms of Mean Absolute Percentage Error (MAPE) was conducted and the best result was found to be 0.2887%. Reliable area-specific solar power production map can provide better utilization of solar energy resource and help in power system management.