摘要
光伏发电功率预测对提高光伏电站控制、调度性能以及保证电网的安全稳定运行具有重要意义。提出一种基于相似日和回声状态网络(ESN)的光伏发电功率预测模型。首先运用相关性分析法对光伏发电功率的影响因素进行了深入分析,并筛选出其主要影响因素;再利用主要影响因素的历史气象信息建立气象特征向量,通过计算灰色关联度(GRA)寻找合适的相似日;最后运用ESN创建预测模型,利用相似日历史数据训练ESN,而后对预测日的输出功率进行逐时预测。算例表明,对比传统模型,GRA-ESN模型具有更高的预测精度和更好的可行性。
Photovoltaic power generation forecasting is of great significance for improving the control and dispatching performance of photovoltaic power plants and ensuring the safe and stable operation of power grid.A photovoltaic power generation prediction model is proposed based on Echo State Networks(ESN).Firstly,the correlation analysis method is used to analyze the influencing factors of photovoltaic power generation,and the main influencing factors are selected.The meteorological feature vector is established by using the historical meteorological information of the main influencing factors,and the appropriate similar day is found by calculating the gray correlation degree.Finally,ESN is used to create a predictive model,and the similar day historical data is used to train the echo state network,and then the output power of the forecast day is predicted on a time-by-time basis.The empirical analysis shows that the GRAESN model has higher prediction accuracy and better feasibility than the traditional prediction model.
作者
安鹏跃
孙堃
AN Pengyue;SUN Kun(Institute of Economics and Management,North China Electric Power University,Beijing 102206,China;New Energy Power and Low Carbon Development Research Center,North China Electric Power University,Beijing 102206,China)
出处
《智慧电力》
北大核心
2020年第8期38-43,共6页
Smart Power
基金
国家自然科学基金资助项目(71772060)。
关键词
光伏功率预测
相似日
灰色关联分析
回声状态网络
photovoltaic power prediction
similar day
grey correlation analysis
echo state network