摘要
太阳辐射量受到季节、大气状况、云况、温度、湿度甚至沙尘等气象因素的影响,表现为强烈的时变性和随机性。对于非线性的辐射量预测,目前已提出了许多方法,但依然存在智能算法的选取不合理、网络结构泛化能力差、预测精度不理想等不足。针对光伏电站太阳逐时辐射强度数据特征不明显、普通BP网络难以完全映射其特征的缺点,提出了一种基于小波包-神经网络的预测模型(WPNN),利用小波包变换将辐射强度序列进行多尺度分解,并创建多个BP模型对各分量预测,最后通过重构得到最终的预测结果。结果表明,预测精度明显提高,满足预期效果,证明该模型的有效性和实际意义。
The amount of solar radiation is affected by the season, atmospheric conditions, cloud conditions, tempera- ture, humidity and even dust and other weather factors with a strong time variability and randomness. For the predic- tion method of nonlinear radiation, many methods are put forward currently. However, the following problems are still presented, such as the intelligent algorithm selecting is unreasonable, network structure generalization ability is poor and prediction accuracy is not ideal. In view of the deficiency of that a photovohaic power station solar radiation inten- sity of the original hourly data is not obvious and the normal BP neural network can not be completely mapped its fea- tures, this paper puts forward a prediction model based on Wavelet Packet Neural Network （WPNN）. It used wavelet packet to transform the radiation intensity sequence multi-scale decomposition and established several BP neural net- work models to forecast each frequency components, and obtaining the complete prediction value with the wavelet packet reconstruction finally. The results show that the prediction accuracy is significantly improved to meet the expec- ted results, which demonstrates the effectiveness and practical value of the model.
作者
陈杰
张新燕
吕光建
Chen Jie Zhang Xinyan LvGuangjlan(College of Electrical Engineering, Xin fiang University, Urumqi 830047, China Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Technology, Urumqi 830047, China)
出处
《电测与仪表》
北大核心
2016年第17期49-54,共6页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(51367015)
新疆维吾尔自治区科技支疆项目(201491112)
国网新疆电网项目(SGXJ0000DKJS1440234)
关键词
太阳辐射
预测
小波包变换
神经网络
solar radiation, prediction, wavelet transform, neural network.
