{"title":"Automatic Multi-steps Prediction Modelling for Wind Power Forecasting","authors":"Shuwen Zheng, Jie Liu","doi":"10.1109/ISSSR53171.2021.00018","DOIUrl":null,"url":null,"abstract":"Wind power is an important source of renewable energy. Owing to the randomness of wind speed, wind power forecasting has always been a challenging issue and is of paramount significance to the operation safety of power systems. In this paper, we proposed a hybrid method for multi-steps wind power forecasting, which combines the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory (LSTM) neural network with modified Genetic Algorithm optimization. The unknown parameters of LSTM and component aggregation weights in result reconstruction are optimized to improve the forecasting performance. A case study concerning the real wind power datasets from ELIA is carried out to validate the effectiveness of the proposed method.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 7th International Symposium on System and Software Reliability (ISSSR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSSR53171.2021.00018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Wind power is an important source of renewable energy. Owing to the randomness of wind speed, wind power forecasting has always been a challenging issue and is of paramount significance to the operation safety of power systems. In this paper, we proposed a hybrid method for multi-steps wind power forecasting, which combines the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory (LSTM) neural network with modified Genetic Algorithm optimization. The unknown parameters of LSTM and component aggregation weights in result reconstruction are optimized to improve the forecasting performance. A case study concerning the real wind power datasets from ELIA is carried out to validate the effectiveness of the proposed method.