Input parameters selection and accuracy enhancement techniques in PV forecasting using Artificial Neural Network

As the demand and deployment of renewables get better, there comes a growing interest in improved techniques of forecasting the energy generation from those sources. This paper aims at testing and suggesting techniques for input parameter selection and accuracy enhancement in forecasting power output of a PV system. The PV system under study is an operational system in Goldwind smart microgrid in Beijing, China. Historical records of PV power and weather data are utilized while the forecasting models are based on ANN. The paper starts with studying selection of input parameters through correlation analysis, sets of sensitivity analysis techniques and Garson's algorithm. A combination of these methods was able to pick out the most important parameters in deciding the PV power amount. In order to boost accuracy of forecasting, in addition to the recommended strategic input selection method and searching for optimal size of network, options of output processing were tested. The use of more than one network with different training algorithm and using different types of ANN were investigated with both techniques resulting in enhancement in accuracy of forecast.