采用改进的黄金搜索优化算法优化的埃尔曼神经网络，用于混合可再生能源系统的优化设计

IF 2.3 4区工程技术 Q3 ENERGY & FUELS

Energy Sources Part A-recovery Utilization and Environmental Effects Pub Date : 2024-07-12 DOI:10.1080/15567036.2024.2375740

Suxia Chen, Jiachen Zhang, Guijie Zhang, Homayoun Ebrahimian

引用次数: 0

摘要

混合可再生能源系统（HRESs）为应对提供可靠、经济和可持续能源的挑战提供了一种前景广阔的解决方案。将这些技术集成到可再生能源系统中，将有助于提高能源效率。

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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Elman neural network optimized by improved golden search optimization algorithm for optimal designing of the hybrid renewable energy systems

Hybrid renewable energy systems (HRESs) could offer a promising solution to the challenges of providing a dependable, economical and sustainable energy’s source. The integration of these technologi...

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来源期刊

Energy Sources Part A-recovery Utilization and Environmental Effects 工程技术-工程：化工

CiteScore

4.40

自引率

6.90%

发文量

488

审稿时长

1.7 months

期刊介绍： Energy Sources Part A: Recovery, Utilization, and Environmental Effects aims to investigate resolutions for the continuing increase in worldwide demand for energy, the diminishing accessibility of natural energy resources, and the growing impact of energy use on the environment. You are invited to submit manuscripts that explore the technological, scientific and environmental aspects of: Coal energy sources Geothermal energy sources Natural gas Nuclear energy sources Oil shale energy sources Organic waste from energy use Petroleum Solar energy sources Tar utilization Sand utilization Wind energy.