摘要
数值天气预报(Numerical Weather Prediction,NWP)被广泛应用于光伏发电功率预测,对于电网经济调度具有重要意义。数值方法存在一定的误差,其中日前太阳辐照度作为光伏发电功率预测最直接的因素,会导致预测精度下降。基于历史天气预报数据,推理天气预报中的误差和Kmeans对历史相似日聚类,提出一种日前太阳辐照度修正方法,并在此基础上提出一种可分解输入序列趋势与季节分量的COSTLSTM模型用于太阳辐照度修正模型。通过某公司现场采集和某平台数值气象预报数据进行实验验证,与LinearRegression、KernelRidge、GradientBoosting等传统方法相比,所提方法具有更高的鲁棒性和准确性。
Numerical weather prediction(NWP)is widely applied in photovoltaic power generation forecasting,and it holds significant importance for the economic dispatch of power grids.Due to the unavoidable inherent errors in numerical methods,the day-ahead solar irradiance—the most direct factor affecting photovoltaic power generation forecasting—leads to a reduction in prediction accuracy.This study proposes a day-ahead solar irradiance correction method based on inferring forecast errors from historical weather forecast data and clustering historically similar days using K-means.On this basis,a COSTLSTM model capable of decomposing trend and seasonal components of input sequences is further proposed for the solar irradiance correction model.Experimental verification was conducted using on-site collected data from a company and numerical meteorological forecast data from a platform.Compared with traditional methods such as LinearRegression,KernelRidge,and GradientBoosting,the method proposed in this study exhibits higher robustness and accuracy.
作者
黄飞
陈汝建
万俊杰
HUANG Fei;CHEN Rujian;WAN Junjie(Central Southern China Electric Power Design Instute Co.,Ltd.,China Power Engineering Consulting Group,Wuhan 430071,China;Acrel Electric Co.,Ltd.,Shanghai 201801,China)
出处
《电气应用》
2026年第1期75-82,共8页
Electrotechnical Application
