Abstract
To improve the accuracy of photovoltaic power prediction under haze weather, a short-term PV power prediction method combining the PM2.5 forecast and SVR model is proposed. The WRF-CHEM air quality model is used to realize the simulation calculation of PM2.5 concentration, combined with the historical output data of PV power plants, and based on the SVR machine learning model to build a direct mapping relationship model between multiple meteorological elements and PV output. A forecast experiment was carried out for a certain area in North China, and the results showed that the proposed method can effectively improve the accuracy of PV power forecasting under haze weather conditions, thereby providing strong support for power grid dispatch and operation.
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