Assessing the Variations in Long-Term Photovoltaic Yield Prediction Due to Solar Irradiance and Module Temperature
Abstract
To mitigate the financial investment risks for PV systems stakeholders, it is a prerequisite to reliably predict the long-term energy yield (LTYP). However, this is not usually the case due to several different influencing effects such as: solar resource, system design, the quality of the components as well as degradation. All these effects increase the uncertainties in LTYP. In order to improve the prediction accuracy, each of these effects should be separately explored. The main aim of this study is to assess the effects solar irradiance used a representative for LTYP. In the manuscript we show that using solar irradiance recurrence approach that include year-to-year climate variability reduced the variations to less than 5% as compared to the typical meteorological year approach with variations of more than 10%. The effect of temperature correction in LTYP model led to less than 0.1% variations in comparison with a non-temperature corrected model.