中国电机工程学报Issue(3)：561-567,7.DOI:10.13334/j.0258-8013.pcsee.2015.03.007
地基云图结合径向基函数人工神经网络的光伏功率超短期预测模型
A Model of Very Short-term Photovoltaic Power Forecasting Based on Ground-based Cloud Images and RBF Neural Network
摘要
Abstract
Due to many local random factors’ effect, the very short-term photovoltaic power forecasting is facing great challenges. Cloud is one of the main factors that makes the surface irradiance fluctuate randomly, thereby causing random changes in output of photovoltaic power, so the cloud need to be quantified and taken into account in the modeling of photovoltaic power forecasting. Firstly, based on all-sky cloud images, the image features related to ground irradiance were extracted using digital image processing techniques. And then the radical basis function (RBF) neural network forecasting model was established, in which the input factors consist of extraterrestrial irradiation, air mass and cloud image features such as image brightness and cloud amount, and the surface irradiance was as output factor. Finally, the very short-term photovoltaic power forecasting was achieved by conversion model of irradiance and power. The experimental results show that, the performance of photovoltaic power forecasting model taking into account the cloud image information was better than the model without any image information. So an important approach was proposed for very short-term photovoltaic power precisely forecast.关键词
地基云图/人工神经网络/光伏功率预测/超短期Key words
ground-based cloud images/artificial neural network/photovoltaic power forecast/very short-term分类
信息技术与安全科学引用本文复制引用
陈志宝,丁杰,周海,程序,朱想..地基云图结合径向基函数人工神经网络的光伏功率超短期预测模型[J].中国电机工程学报,2015,(3):561-567,7.基金项目
国家863高技术基金项目(2011AA05A104)；中国电力科学研究院科技创新基金项目(YN83-14-006)。@@@@The National High Technology Research and Development of China 863 Program (2011AA05A104) （2011AA05A104）
Project Supported by Science and Technology Innovation Fund of China Electric Power Research Institute (YN83-14-006) （YN83-14-006）
