电力系统自动化2018，Vol.42Issue(6)：53-59,7.DOI:10.7500/AEPS20170605001
基于风速云模型相似日的短期风电功率预测方法
Short-term Wind Power Prediction Method Based on Wind Speed Cloud Model in Similar Day
摘要
Abstract
Wind power prediction is the important basis and necessary means to solve the influence of wind power uncertainty. The requirement to prediction accuracy at each time point will be stricter with high-proportion wind power integration. Training samples are one of the key factors influencing the prediction accuracy.However,because of the complex diversity and ambiguity of the actual weather system,it is very important for improving the prediction accuracy to select the training samples that are similar to the wind conditions in the scheduling period.Therefore,a method based on wind speed cloud model is proposed to directionally select training samples with similar wind speed features in a given day.This method can improve the learning ability in capturing the randomness and fuzziness of wind speed in designated time period.By screening the historical data in an oriented way,the proposed method can enhance the prediction accuracy.Firstly,a historical database for daily wind speed cloud models is created.Then,the similarity of the wind speed cloud models is quantified,and this quantification index is used to select a sequence of historical data being similar to the wind in a predicted period.The selected samples will be used as training samples for short-term wind power prediction.In the practice,the training samples and prediction models are updated according to the weather prediction information so as to improve the prediction accuracy.The results of an example of a wind farm in Northern China are demonstrated,and verify that the proposed method can improve the accuracy of short-term wind power prediction and has practical value in engineering applications.关键词
风电功率预测/风速云模型/相似日/训练样本/样本定向选取Key words
wind power prediction/cloud model of wind speed/similar day/training sample/sample orientation selection引用本文复制引用
阎洁,许成志,刘永前,韩爽,李莉..基于风速云模型相似日的短期风电功率预测方法[J].电力系统自动化,2018,42(6):53-59,7.基金项目
国家重点研发计划资助项目(2016YFB0900100) （2016YFB0900100）
国家自然科学基金青年科学基金资助项目(51707063) （51707063）
中央高校基本科研业务费专项资金资助项目(2017MS024).This work is supported by National Key R&D Program of China(No.2016YFB0900100),National Natural Science Foundation of China(No.51707063)and Fundamental Research Funds for the Central Universities(No.2017MS024). （2017MS024）
