摘要
数值天气预报(numerical weather prediction,NWP)是短期风电功率预测模型的主要输入。通常,传统模型只考虑NWP的风速、风向、温度、湿度、压强这5类典型特征,且多数在单位置NWP的基础上建立。因此,为充分利用NWP信息,研究了NWP非典型特征的可用性,并考虑了多个位置的NWP信息。在考虑多位置NWP及非典型特征时,提出了以最大相关-最小冗余原则提取输入变量的预测方法,并和通过主成分分析提取的方法进行对比。结果表明,多位置NWP和非典型特征均包含有效信息,有利于提高预测精度。而在考虑多位置NWP和非典型特征时,以最大相关-最小冗余原则建立的模型比通过主成分分析建立的模型预测精度更高,其均方根误差较只考虑单位置NWP和典型特征时降低了1.84%。
Numerical weather prediction(NWP) is the main input of short-term wind power forecasting model. Usually, only five canonical features in the NWP, namely wind speed, wind direction, temperature, humidity and pressure, are considered in conventional models, most of which are established based on the NWP of single location. In order to make full use of NWP information, applicability of uncanonical features is investigated and the NWP information of multiple locations is considered. By combining the NWP information of multiple locations with uncanonical features, the method where models are established based on input variables obtained with the principle of minimal redundancy and maximal relevance is proposed and compared with the method based on principal component analysis. Results show that NWP of multiple locations and its uncanonical features contain useful information, beneficial to improve prediction accuracy. By considering the NWP of multiple locations and uncanonical features, the forecasting model based on principle of minimal redundancy and maximal relevance shows advantages over the model based on principal component analysis and improves its RMSE by 1.84%, compared with the model only considering NWP of single location with canonical features.
作者
宋家康
彭勇刚
蔡宏达
夏杨红
王晓明
SONG Jiakang;PENG Yonggang;CAI Hongda;XIA Yanghong;WANG Xiaoming(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,Zhejiang Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2018年第10期3234-3240,共7页
Power System Technology
基金
国家重点研发计划项目(2017YFB0903300)
浙江省重点研发计划项目(2017C01039)
浙江省自然科学基金项目(LY16E070002)~~
关键词
短期风电功率预测
非典型特征
多位置
特征选择
数值天气预报
short-term wind power forecasting
uncanonical feature
multiple locations
feature selection
numerical weather prediction