Empirical-Based Approach for Prediction of Global Irradiance and Energy for Solar Photovoltaic Systems

Abstract

Accuracy in prediction of global horizontal irradiance is vitally important for photovoltaic energy prediction, its installation and pre-sizing studies. A change in the solar radiation directly impacts the electricity production and in turn, the plant economics. Hence employing a model possessing improved prediction accuracy significantly affects the photovoltaic energy prediction. Furthermore, monthly mean data is required for prediction of long-term performance of solar photovoltaic systems, making the same to be concentrated for the present contribution. The available models for prediction of irradiance and energy unlike physical and statistical models depend on input parameters whose availability, assumption and determination is difficult. This finally creates complexity in predicting the desired response. Hence empirical models are chosen preferable over physical and statistical-based models. Empirical models correlate only the available input atmospheric parameters affecting solar irradiance and energy, thereby reducing the complexity experienced by physical and statistical models. Yet, the reliability or accuracy of model varies with location. The reliability of an empirical model depends on the incorporation of input’s and data set (training set) for its formulation. Thus the consideration of significant input factors lies to be a persistently prevailing challenge, driving the need for an improved prediction model delivering irradiance and energy. In this chapter, an empirical model is proposed for prediction of irradiance and energy. The incorporated input factors for the formulation of energy prediction model is emphasized by performance and exergy analysis of solar photovoltaic systems. The proposed model hence combines the thermal and electrical aspects of photovoltaic systems gaining reliability and limiting the dependence towards real-time measured input factors.