Photovoltaic power generation power prediction method and related product

Abstract

The invention discloses a photovoltaic power generation power prediction method and related products, and the method comprises the steps: collecting the operation data, weather values and satellite data of a photovoltaic power station, converting the data into meteorological parameters through employing an inversion algorithm, and then carrying out the calculation of the meteorological parameters; and inputting the weather values and the meteorological parameters of the photovoltaic power station into a pre-constructed mesoscale numerical simulation and variation assimilation model so as to obtain predicted meteorological data. And finally, inputting the predicted meteorological data and the operation data of the photovoltaic power station into the machine learning model, and predicting the generated power of the photovoltaic power station. According to the invention, by integrating multiple data sources and combining the mesoscale numerical simulation technology and the machine learning algorithm, the accuracy and reliability of photovoltaic power generation power prediction are improved, and powerful support is provided for scheduling and management of photovoltaic power generation.

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