Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization. (20th December 2020) - Pan, Mingzhang; Li, Chao; Gao, Ran; Huang, Yuting; You, Hui; Gu, Tangsheng; Qin, Fengren
Abstract: Accurate prediction of photovoltaic (PV) power for an ultra-short term can improve the usage of grid-connected PV power. In this study, data preprocessing based on an ultra-short-term PV model is explored. A support vector machine (SVM) is constructed based on the processed data, and the parameters of the SVM are optimized using ant colony optimization (ACO). A series of improvements are introduced to optimize the ACO. The results indicate that the regression coefficient (R 2 ) of the model can be increased by 6.8% through reasonable data preprocessing. However, smoothing is not suitable for the preprocessing of PV models with large datasets. The R 2 of the hybrid model reaches up to 0.997. In particular, the forecasting accuracies for peak power and nighttime are significantly improved, thereby improving the model's full-time grid-connected generation abilities. Highlights: The I-ACO-SVM model is proposed for forecasting PV power. I-ACO is used to optimize the parameters. The process of data preprocessing is optimized. I-ACO-SVM improves the accuracy of forecasting at peak power.