The Impact of Meteorological Data on the Accuracy of Solar Electricity Generation Forecasting Using Neural Networks

The resolution of key tasks related to energy resource conservation and the enhancement of energy security is not possible without the widespread use of the electricity generated by renewable energy sources (RES), including, among others, photovoltaic (solar) power plants. A negative aspect of connecting renewable energy sources to power grids is the challenge of forecasting their generation, which consequently makes it difficult to plan the stable operation of power systems with distributed generation. To improve the accuracy of forecasting electricity generation by solar power plants, this article proposes the use, in addition to meteorological parameters such as air temperature and wind speed, of a new indicator defined as the unit active power generation ( P* ). This indicator can be used in the development of mathematical models and methods to increase the energy efficiency of power systems utilizing renewable energy sources, particularly to enhance the accuracy of power generation forecasting processes using neural networks and machine learning. The article shows that the use of this indicator allows for increasing the accuracy of forecasting energy production by solar power plants.