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Alternative TitlePhotovoltaic generation prediction based on similar days and neural network
李鹏梅; 臧传治; 王侃侃
Source Publication可再生能源
Volume31Issue:10Pages:1-4, 9
Contribution Rank1
Funding Organization国家自然科学基金(61100159);中国科学院知识创新工程重要方向性项目(KGCX2-EW-104)
Keyword光伏发电 相似日原理 Bp神经网络 功率预测


Other Abstract

Output power of photovoltaic (PV) power generating system has the characteristics of time varying, intermittence and randomness due to the various meteorological factors such as season, solar radiation, temperature, humidity, etc. In this paper, a forecasting method is proposed based on the principle of similar days and BP neural network. By using historical weather information from the solar power station, meteorological feature vectors are established, and similar days are found based on Manhattan distance. According to the given different forecasting day, output power of three similar days would be chosen as inputs of the forecasting model, and then the output power of generating station can be predicted directly. A forecasting model is made based on a photovoltaic power station and the forecast error is calculated and analyzed. The results show the validity of the algorithm.

Document Type期刊论文
Recommended Citation
GB/T 7714
李鹏梅,臧传治,王侃侃. 基于相似日和神经网络的光伏发电预测[J]. 可再生能源,2013,31(10):1-4, 9.
APA 李鹏梅,臧传治,&王侃侃.(2013).基于相似日和神经网络的光伏发电预测.可再生能源,31(10),1-4, 9.
MLA 李鹏梅,et al."基于相似日和神经网络的光伏发电预测".可再生能源 31.10(2013):1-4, 9.
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