Lingyun Jia, Sining Yun, Zeni Zhao, Jiaxin Guo, Yao Meng, Xuejuan Li, Jiarong Shi, Ning He, Liu Yang
{"title":"利用 EEMD-BiGRU 模型改进太阳能发电的短期预测：基于七个独立模型和六个混合模型的比较研究","authors":"Lingyun Jia, Sining Yun, Zeni Zhao, Jiaxin Guo, Yao Meng, Xuejuan Li, Jiarong Shi, Ning He, Liu Yang","doi":"10.1080/15435075.2024.2356103","DOIUrl":null,"url":null,"abstract":"Accurate and timely forecasting is critical for grid-connected solar power safety and stability, achieved through machine learning (ML) for both common and real-time applications. To mitigate the i...","PeriodicalId":14000,"journal":{"name":"International Journal of Green Energy","volume":"38 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving short-term forecasting of solar power generation by using an EEMD-BiGRU model: A comparative study based on seven standalone models and six hybrid models\",\"authors\":\"Lingyun Jia, Sining Yun, Zeni Zhao, Jiaxin Guo, Yao Meng, Xuejuan Li, Jiarong Shi, Ning He, Liu Yang\",\"doi\":\"10.1080/15435075.2024.2356103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate and timely forecasting is critical for grid-connected solar power safety and stability, achieved through machine learning (ML) for both common and real-time applications. To mitigate the i...\",\"PeriodicalId\":14000,\"journal\":{\"name\":\"International Journal of Green Energy\",\"volume\":\"38 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Green Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/15435075.2024.2356103\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Green Energy","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/15435075.2024.2356103","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Improving short-term forecasting of solar power generation by using an EEMD-BiGRU model: A comparative study based on seven standalone models and six hybrid models
Accurate and timely forecasting is critical for grid-connected solar power safety and stability, achieved through machine learning (ML) for both common and real-time applications. To mitigate the i...
期刊介绍：
International Journal of Green Energy shares multidisciplinary research results in the fields of energy research, energy conversion, energy management, and energy conservation, with a particular interest in advanced, environmentally friendly energy technologies. We publish research that focuses on the forms and utilizations of energy that have no, minimal, or reduced impact on environment, economy and society.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
