遗传修饰优化技术:神经网络多目标能源管理方法

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-08-22 DOI:10.1016/j.egyai.2024.100417
Mutaz AlShafeey , Omar Rashdan
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引用次数: 0

摘要

本研究针对多目标能源资源管理引入了神经网络增强基因修饰优化技术。为了满足对可持续能源解决方案的需求,该技术集成了神经网络模型作为适应度函数,代表了人工智能驱动的优化技术的进步。在欧盟收集的数据包括温室气体排放、能源消耗、能源进口和能源平准化成本。由于不同的能源消耗配置会导致不同的温室气体排放量、成本和进口量,因此使用神经网络预测模型来预测新能源组合对这些变量的影响。然后将预测结果输入基因修饰优化过程,以确定最佳配置。经过 28 代的模拟,能源成本降低了 46%,排放量减少了 9%。参数设置的自动化减轻了人为偏差和主观性,提高了结果的客观性。与传统方法(如欧氏距离)的基准对比验证了这种方法的卓越性能。此外,该技术还能将染色体和基因值可视化,使优化过程更加清晰。这些结果表明,该技术在能源领域取得了重大进展,并有可能应用于其他行业,为全球应对气候变化做出贡献。
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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Genetic modification optimization technique: A neural network multi-objective energy management approach

In this study, a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multi-objective energy resource management. Addressing the need for sustainable energy solutions, this technique integrated neural network models as fitness functions, representing an advancement in artificial intelligence-driven optimization. Data collected in the European Union covered greenhouse gas emissions, energy consumption by sources, energy imports, and Levelized Cost of Energy. Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions, costs, and imports, neural network prediction models were used to project the effect of new energy combinations on these variables. The projections were then fed into the gene modification optimization process to identify optimal configurations. Over 28 generations, simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions. Human bias and subjectivity were mitigated by automating parameter settings, enhancing the objectivity of results. Benchmarking against traditional methods, such as Euclidean Distance, validated the superior performance of this approach. Furthermore, the technique's ability to visualize chromosomes and gene values offered clarity in optimization processes. These results suggest significant advancements in the energy sector and potential applications in other industries, contributing to the global effort to combat climate change.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
期刊最新文献
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