摘要
精确地预测极端天气下的风速能为配电网防灾抗灾提供重要的指导作用.本文提出基于时间卷积网络(Temporal Convolutional Network,TCN)与双向长短期记忆网络(Bi-directional Long Short-Term Memory,BiLSTM)和误差修正的组合模型对极端天气下的风速进行预测.首先对天气数据进行预处理,用TCN提取多特征数据的时间序列特性,将提取信息输入到BiLSTM中进行风速预测.为进一步提高预测精度,引入变分模态分解(Variational Mode Decomposition,VMD)对误差序列进行分解,分别对分解后的误差子序列构建BiLSTM模型进行误差预测,用误差预测值对风速预测值进行误差修正.结合河南省某地实测天气数据进行实验,仿真结果验证了所提方法能有效预测风速,并在极端天气发生时,对风速具有较高的预测精度.
Accurate prediction of wind speed in extreme weather can provide important guidance for distribution network to enhance disaster prevention and resilience.This paper proposes a method based on Temporal Convolutional Network(TCN),Bi-directional Long Short-Term Memory(BiLSTM)and error correction for wind speed prediction in extreme weather.First,the time series characteristics of multi-feature weather data are extracted by TCN,and then input into BiLSTM for wind speed prediction.To further improve the prediction accuracy,Variational Mode Decomposition(VMD)is introduced to decompose the error sequence,and BiLSTM models are constructed to perform error prediction for the decomposed error subsequences respectively.Then the error prediction value is used to correct the wind speed prediction value.Finally,simulations are carried out for a place of Henan province,and the results show that compared with measured weather data,the proposed method can effectively predict wind speed with high accuracy when extreme weather occurs.
作者
刘善峰
李哲
陈锦鹏
卢明
向玲
LIU Shanfeng;LI Zhe;CHEN Jinpeng;LU Ming;XIANG Ling(Electric Power Research Institute of State Grid Henan Electric Power Company,Zhengzhou 450052,China;School of Energy Power and Mechanical Engineering,North China Electric Power University,Baoding 071003,China)
出处
《南京信息工程大学学报（自然科学版）》
CAS
北大核心
2023年第5期574-584,共11页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
国家电网有限公司科技指南项目(5400-202199555A-0-5-ZN)。
关键词
风速
时间卷积网络
双向长短期记忆网络
误差修正
变分模态分解
预测
wind speed
temporal convolutional network(TCN)
bi-directional long short-term memory(BiLSTM)
error correction
variational mode decomposition(VMD)
prediction
