Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant. (1st September 2004)

• Record Type: Journal Article
• Authors: Ashraf, Imtiaz; Chandra, A.
• Is Part Of: International journal of global energy issues. Volume 21:Number 1/2(2004)
• Page Start: 119
• Page End: 130
• Publication Date: 2004-09-01
• Subjects: artificial neural networks -- electrical energy generated output -- forecasting -- univariate modelling -- solar energy -- photovoltaics; Power resources -- Periodicals; 333.79
• Languages: English
• ISSNs: 0954-7118

Abstract

This paper presents an artificial neural network (ANN) approach for forecasting the performance of electric energy generated output from a working 25-kWp grid connected solar PV system and a 100-kWp grid connected PV system installed at Minicoy Island of Union Territory of Lakshadweep Islands. The ANN interpolates among the solar PV generation output and relevant parameters such as solar radiation, module temperature and clearness index. In this study, three ANN models are implemented and validated with reasonable accuracy on real electric energy generation output data. The first model is univariate based on solar radiation and the output values. The second model is a multivariate model based on module temperature along with solar radiation. The third model is also a multivariate model based on module temperature, solar radiation and clearness index. A forecasting performance measure such as percentage root mean square error has been presented for each model. The second model, which gives the most accurate results, has been used in forecasting the generation output for another PV system with similar accuracy.