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Renewable energy sources (RES) are continuously gaining in importance, especially because diversification of energy supply through RES ensures sustainability. However, the ever-increasing interest in increase of RES share, mandates the integration and proper control of systems that exploit multiple technologies (e.g. photovoltaic, solar thermal, wind). To strengthen these control processes the short-term forecast of possible energy supply from specific RES is very important. Currently, there are several complex methods based on artificial intelligence that may be used for forecasting of energy production. However, there is no available methodology to determine the actual energy output of existing PVs system that is accurate, simple, low cost, easy to implement and that is based on physical dependencies.
This work makes progress in this regard and presents a method for creating a real energy output model of photovoltaic systems in the form of an equivalent solar irradiance. The proposed model is simple, accurate, low-cost, easy to integrate, and versatile. It is based on physical dependent parameters, which may be used for the prediction of the energy yield of such energy systems.
The model was obtained using detailed data obtained from a 1.0 MW photovoltaic farm located in Lubelskie Voivodeship (Poland), which is equipped with various types of photovoltaic technologies. The first step was to determine the influence of individual weather parameters such as solar irradiance, wind, outdoor air temperature and humidity, on the energy yield of photovoltaic systems, under different combinations. The multiparametric analysis of all external factors gave the best results with a mean absolute percentage error of model predictions against real energy output at 15.3 % and 16.5 % for two different types of PV modules.
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