A Novel Model for Daily Energy Production Estimation of Grid Connected Photovoltaic System

Abstract

The rapidly growing markets for distributed and centralized gridconnected photovoltaic (PV) systems require the reliable and available information for reflecting and predicting the electricity generation of PV systems. In this work, the relationship between PV energy production and meteorological environmental factors is discussed by correlation analysis and partial correlation analysis. Meteorological data available, including the clearness index, diurnal temperature range, the global radiation on horizontal surface, and etc., are used as inputs. Then, according to factor analysis, these various interaction factors are extracted as two independent common factors. Finally, a new method based on factor analysis and multiple regression analysis has been developed for estimating the daily PV energy production. The meteorological data are collected from Wuhan Observatory, and power data from a roof gridconnected PV system located at Huazhong University of Science and Technology in Wuhan. The data of the whole year (from March in 2010 to February in 2011) has been used for model calibration and the following data of March in 2011 is used to test the predictions. The results show that there is significant positive correlation between the estimated values and the measured values; the rMBE per day is −0.14%, MAPE per day is 13.60% and rRMSE per day is 18.04%.