Micro-Scale Weather Condition Recognition Based Ultra-Short-Term Power Forecast of Solar Photovoltaic Power Plant
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1State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, Hebei, China
2Yingli Green Energy Holding Co Ltd, Baoding 071051, Hebei, China
3Yunnan Electric Power Test and Research Institute (Group) Co Ltd. Electric Power Research Institute, Kunming 650217, Yunnan, China
A novel ultra-short-term power forecast (UST-PF) approach for solar photovoltaic (PV) power plant is presented. Micro-scale weather condition of the closest time horizon to the next forecast is identified through support vector machine (SVM) classification model whose input vector is composed of four characteristic parameters defined using irradiance data after the previous forecast of the last time. According to the classification results, the appropriate model for the next forecast is determined among multiple forecast models constructed corresponding to different micro-scale weather conditions. Simulation results verify the effectiveness of the proposed approach
How to cite this article
Wang, F., Mi, Z., Yu, B., Su, S., Zhang, L. and Zhang, C. (2012). 'Micro-Scale Weather Condition Recognition Based Ultra-Short-Term Power Forecast of Solar Photovoltaic Power Plant'. SET Proceedings, 11(1), pp. 2324-2330. https://doi.org/10.65582/set.2012.245
