宋家康;彭勇刚;蔡宏达;夏杨红;王晓明;

• 1:浙江大学电气工程学院

摘要(Abstract)：

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%。

关键词(KeyWords)： 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

Abstract:

Keywords:

基金项目(Foundation): 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)~~

作者(Author): 宋家康;彭勇刚;蔡宏达;夏杨红;王晓明;

Email:

DOI: 10.13335/j.1000-3673.pst.2018.0492

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