Bingqing Wang, Jing Liu, Yongping Li, Guohe Huang, Guan Wang
{"title":"基于主成分回归的风电发电主因素识别——以厦门市为例","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":"{\"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}","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}
Identifying Main Factors of Wind Power Generation Based on Principal Component Regression: A Case Study of Xiamen
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
