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基于相似日和神经网络的光伏发电预测
Alternative TitlePhotovoltaic generation prediction based on similar days and neural network
李鹏梅; 臧传治; 王侃侃
Department工业控制网络与系统研究室
Source Publication可再生能源
ISSN1671-5292
2013
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
Abstract

光伏发电系统的输出功率受到季节、太阳辐射强度、温度和湿度等气象条件影响,呈现出时变性、间歇性和随机性。文章提出了基于相似日原理和改进的BP神经网络预测方法,利用光伏电站的历史气象信息建立气象特征向量,基于曼哈顿距离寻找相似日,根据给定的不同预测日选取3个相似日的输出功率作为预测模型输入,直接预测发电站的输出功率。以某光伏电站为例进行建模预测,并通过预测误差分析证明了算法的有效性。

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.

Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/14002
Collection工业控制网络与系统研究室
Affiliation1.中国科学院沈阳自动化研究所,中国科学院网络化控制系统重点实验室
2.中国科学院大学
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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