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

IF 3.1 4区工程技术 Q3 ENERGY & FUELS

International Journal of Green Energy Pub Date : 2024-05-27 DOI:10.1080/15435075.2024.2356103

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}

引用次数: 0

摘要

准确、及时的预测对于并网太阳能发电的安全性和稳定性至关重要，这可以通过机器学习（ML）在普通和实时应用中实现。为了减轻并网发电的不稳定性，我们需要对并网太阳能发电的...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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...

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Green Energy 工程技术-能源与燃料

CiteScore

6.60

自引率

9.10%

发文量

112

审稿时长

3.7 months

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.