基于ELM和误差校正的风力发电区间预测方法
Wind Power Generation Interval Prediction Method Based on ELM and Error Correction
胡子延 1温蜜 1魏敏捷2
作者信息
- 1. 上海电力大学计算机科学与技术学院,上海 200090
- 2. 上海电力大学电气工程学院,上海 200090
- 折叠
摘要
风力发电过程中具有不稳定性和随机性,传统的点预测获得的结果准确性欠佳且无法获得预测值的区间波动范围.提出了一种基于极限学习机(ELM)和误差校正的风力发电区间预测方法.首先,使用皮尔逊相关系数挖掘出数据集中重要特征;然后,建立ELM网络生成预测值,将生成的预测值与原始的风力发电功率值作对比得到风力发电功率的误差;再将误差和原始的数据结合成新的数据集输入到已经训练好的ELM网络模型中,得到校正过后的误差;最后,通过所提出的区间构造方法得到风电功率预测区间.仿真结果表明,校正过后的误差比原始误差小,构造出的区间具有较高的可靠性和较窄的区间带宽,能更准确地描述风力发电出力范围.
Abstract
The process of wind power generation has instability and randomness,and traditional point prediction results have poor accuracy and cannot obtain the range of fluctuation of predicted values.This paper proposes an in-terval forecasting method for wind power generation based on extreme learning machine(ELM)and error correction.First,we used the Pearson correlation coefficient to excavate important features in the data set;then,established an ELM network to generate predicted values,and compared the generated predicted values with the original wind power value to obtain the error of wind power.Then the data were combined into a new data set and input into the trained ELM network model to obtain the corrected error.Finally,the wind power prediction interval was obtained by the pro-posed interval construction method.The experimental simulation shows that the corrected error is smaller than the o-riginal error,and the constructed interval has higher reliability and narrower interval bandwidth,which can describe the output range of wind power generation more accurately.
关键词
风力发电/特征挖掘/预测区间/极限学习机/误差校正Key words
Wind power/Feature mining/Prediction interval/Extreme learning machine/Error correction引用本文复制引用
基金项目
国家自然科学基金(61872230)
国家自然科学基金(U1936213)
上海市学术带头人项目(21XD1421500)
上海市科委项目(20020500600)
出版年
2024
NETL
NSTL
万方数据