{"title":"用于太阳辐照度预报的混合机器学习和优化方法","authors":"Chaoyang Zhu, Mengxia Wang, Mengxing Guo, Jinxin Deng, Qipei Du, Wei Wei, Yuxiang Zhang","doi":"10.1080/0305215x.2024.2390126","DOIUrl":null,"url":null,"abstract":"The objective of this study is to investigate a novel hybrid model for the accurate prediction of direct normal irradiance. For this purpose, a decomposition technique, a clustering technique, an o...","PeriodicalId":50521,"journal":{"name":"Engineering Optimization","volume":"59 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid machine learning and optimization method for solar irradiance forecasting\",\"authors\":\"Chaoyang Zhu, Mengxia Wang, Mengxing Guo, Jinxin Deng, Qipei Du, Wei Wei, Yuxiang Zhang\",\"doi\":\"10.1080/0305215x.2024.2390126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this study is to investigate a novel hybrid model for the accurate prediction of direct normal irradiance. For this purpose, a decomposition technique, a clustering technique, an o...\",\"PeriodicalId\":50521,\"journal\":{\"name\":\"Engineering Optimization\",\"volume\":\"59 1\",\"pages\":\"\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Optimization\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/0305215x.2024.2390126\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Optimization","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/0305215x.2024.2390126","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Hybrid machine learning and optimization method for solar irradiance forecasting
The objective of this study is to investigate a novel hybrid model for the accurate prediction of direct normal irradiance. For this purpose, a decomposition technique, a clustering technique, an o...
期刊介绍：
Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as any formalized numerical process for improvement. Algorithms for numerical optimization are therefore mainstream for the journal, but equally welcome are papers which use the methods of operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, artificial intelligence, information theory and processing, and all methods which can be used in the quantitative modelling of the decision-making process.
Innovation in optimization is an essential attribute of all papers but engineering applicability is equally vital. Engineering Optimization aims to cover all disciplines within the engineering community though its main focus is in the areas of environmental, civil, mechanical, aerospace and manufacturing engineering. Papers on both research aspects and practical industrial implementations are welcomed.
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
