用于太阳辐照度预报的混合机器学习和优化方法

IF 2.2 3区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Engineering Optimization Pub Date : 2024-09-04 DOI:10.1080/0305215x.2024.2390126

Chaoyang Zhu, Mengxia Wang, Mengxing Guo, Jinxin Deng, Qipei Du, Wei Wei, Yuxiang Zhang

引用次数: 0

摘要

本研究的目的是研究一种新型混合模型，用于准确预测直接法线辐照度。为此，一种分解技术、一种聚类技术、一种...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Hybrid machine learning and optimization method for solar irradiance forecasting

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来源期刊

Engineering Optimization 管理科学-工程：综合

CiteScore

5.90

自引率

7.40%

发文量

审稿时长

3.5 months

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.