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| Short-Term Photovoltaic Generation Forecasting System Based on Fuzzy Recognition |
| Chen Changsong, Duan Shanxu, Cai Tao, Dai Qian |
| Huazhong University of Science and Technology Wuhan 430074 China |
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Abstract With the increase of the capacity of photovoltaic power generation systems, photovoltaic power prediction is important to the operation of power system with comparatively large amount of photovoltaic power generation. A novel neutral network power forecasting model based on fuzzy recognition is proposed to solve the randomness of photovoltaic power generation. According to historical power and weather data provided by experiments, all factors which influence photovoltaic power generation are discussed and neutral network forecasting module is trained and evaluated. The results indicate that the neutral network structure and the spread value have some impact on the prediction precision and the parameter of fuzzy recognition as input will improve the precision. Forecasting results show the high precision and high efficiency of this forecasting model which is applied in stable operation of photovoltaic power generation system.
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Received: 02 November 2010
Published: 07 March 2014
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2011, Vol. 26 
