{"title":"利用考虑天气因素的 TSO-LSTM-XGBoost 耦合模型预测光伏发电量的研究","authors":"Wenqi Ge, Xiaotong Wang, Yanbai Sun","doi":"10.1080/15435075.2024.2390159","DOIUrl":null,"url":null,"abstract":"The explicit prediction of PV power itself is of great significance to the scheduling and operation of the power grid. To ensure the stable operation of the power system, this paper proposes a coup...","PeriodicalId":14000,"journal":{"name":"International Journal of Green Energy","volume":"30 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on photovoltaic power prediction with TSO-LSTM-XGBoost coupled model accounting for weather factors\",\"authors\":\"Wenqi Ge, Xiaotong Wang, Yanbai Sun\",\"doi\":\"10.1080/15435075.2024.2390159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The explicit prediction of PV power itself is of great significance to the scheduling and operation of the power grid. To ensure the stable operation of the power system, this paper proposes a coup...\",\"PeriodicalId\":14000,\"journal\":{\"name\":\"International Journal of Green Energy\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-08-29\",\"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/10.1080/15435075.2024.2390159\",\"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/10.1080/15435075.2024.2390159","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}
Study on photovoltaic power prediction with TSO-LSTM-XGBoost coupled model accounting for weather factors
The explicit prediction of PV power itself is of great significance to the scheduling and operation of the power grid. To ensure the stable operation of the power system, this paper proposes a coup...
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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.
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