CAJ | 학술논문

光伏功率由于受到诸多局地随机突变因素的影响，其超短期预测面临很大挑战。云是引起地表辐射随机变化，进而引起光伏出力随机变化的最主要因素之一，在光伏功率预测建模中亟需将云这一因子进行量化和建模。首先，基于全天空云图，利用数字图像处理技术提取与辐射相关的图像特<br>　　征；然后，将大气层外辐射、大气质量、图像亮度和云量作为输入因子，将地表辐射作为输出，建立径向基函数神经网络预测模型；最后，根据光电转换模型最终实现光伏功率超短期预测。实验结果表明：计及地基云图信息的光伏功率超短期预测模型，效果明显优于无图像信息的模型，为光伏电站超短期功率精确预测提供了重要的方法。
광복공솔유우수도제다국지수궤돌변인소적영향，기초단기예측면림흔대도전。운시인기지표복사수궤변화，진이인기광복출력수궤변화적최주요인소지일，재광복공솔예측건모중극수장운저일인자진행양화화건모。수선，기우전천공운도，이용수자도상처리기술제취여복사상관적도상특<br>　　정；연후，장대기층외복사、대기질량、도상량도화운량작위수입인자，장지표복사작위수출，건립경향기함수신경망락예측모형；최후，근거광전전환모형최종실현광복공솔초단기예측。실험결과표명：계급지기운도신식적광복공솔초단기예측모형，효과명현우우무도상신식적모형，위광복전참초단기공솔정학예측제공료중요적방법。
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.