Bingqing Wang, Jing Liu, Yongping Li, Guohe Huang, Guan Wang
{"title":"Identifying Main Factors of Wind Power Generation Based on Principal Component Regression: A Case Study of Xiamen","authors":"Bingqing Wang, Jing Liu, Yongping Li, Guohe Huang, Guan Wang","doi":"10.1109/icgea54406.2022.9792108","DOIUrl":null,"url":null,"abstract":"To realize the goals of carbon reduction, it is important for understanding the driving force of the wind power industry. In this study, a principal component regression (PCR) model is employed to identify the main factors of wind power generation in the City of Xiamen. Results disclose that two principal components have a cumulative contribution rate about 95%. The economic component (contributing 81.9%) is dominated by the proportion of secondary industry (SI) and gross domestic product (GDP). The energy component (contributing 12.9%) is dominated by annual wind speed (WS) and the number of patents (NP). Results can provide desired decision support for clean energy utilization and environmental emission reduction.","PeriodicalId":151236,"journal":{"name":"2022 6th International Conference on Green Energy and Applications (ICGEA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 6th International Conference on Green Energy and Applications (ICGEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icgea54406.2022.9792108","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
To realize the goals of carbon reduction, it is important for understanding the driving force of the wind power industry. In this study, a principal component regression (PCR) model is employed to identify the main factors of wind power generation in the City of Xiamen. Results disclose that two principal components have a cumulative contribution rate about 95%. The economic component (contributing 81.9%) is dominated by the proportion of secondary industry (SI) and gross domestic product (GDP). The energy component (contributing 12.9%) is dominated by annual wind speed (WS) and the number of patents (NP). Results can provide desired decision support for clean energy utilization and environmental emission reduction.
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