A Day-Ahead PV Power Forecasting Method Based on Irradiance Correction and Weather Mode Reliability Decision

Accurate day-ahead photovoltaics (PV) power forecasting results are significant for power grid operation. According to different weather modes, the existing research has established a classification forecast framework to improve the accuracy of day-ahead forecasts. However, the existing framework still has the following two problems: (1) weather mode prediction and power forecasting are highly dependent on the accuracy of numerical weather prediction (NWP), but the existing classification forecasting framework ignores the impact from NWP errors; (2) the validity of the classification forecasting framework comes from the accurate prediction of weather modes, but the existing framework lacks the analysis and decision-making mechanism of the reliability of weather mode prediction results, which will lead to a significant decline in the overall accuracy when weather modes are wrongly predicted. Therefore, this paper proposes a day-ahead PV power forecasting method based on irradiance correction and weather mode reliability decision. Firstly, based on the measured irradiance, K-means clustering method is used to obtain the daily actual weather mode labels; secondly, considering the coupling relationship of meteorological elements, the graph convolutional network (GCN) model is used to correct the predicted irradiance by using multiple meteorological elements of NWP data; thirdly, the weather mode label is converted into one-heat code, and a weather mode reliability prediction model based on a convolutional neural network (CNN) is constructed, and then the prediction strategy of the day to be forecasted is decided; finally, based on the weather mode reliability prediction results, transformer model are established for unreliable weather and credible weather respectively. The simulation results of the ablation experiments show that classification prediction is an effective strategy to improve the forecasting accuracy of day-ahead PV output, which can be further improved by adding irradiance correction and weather mode reliability prediction modules.