在热电厂实时实施软计算技术的审查
Love Kumar Thawait1, Mukesh Kumar Singh1
1Department of Industrial and Production Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
概括
本综述总结了人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 如何提高热电厂的生产率. 它分析了2019-2023年的研究,确定了人工智能在发电中的应用的关键领域和未来研究方向.
科学领域:
- 能源工程 能源工程
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 热电厂对于发电至关重要,但面临着生产力挑战.
- 现有的研究探讨了各种方法来提高热电厂的产量.
- 需要进行全面的审查,以综合当前的进展.
研究的目的:
- 为提高热电厂生产率提供AI,ML和DL应用的综合总结.
- 评估传统的基于AI的方法及其贡献.
- 确定AI在热发电领域的研究缺口和未来方向.
主要方法:
- 对2019年至2023年间发表的研究文章进行系统审查.
- 专注于使用AI,ML和DL方法的研究.
- 基于人工智能的方法的比较评估和研究趋势的分析.
主要成果:
- 人工智能,ML和DL显示出在各种维度上提高热电厂生产率的巨大潜力.
- 分析揭示了年度分布和研究重点领域.
- 确定了具有有限和高研究重点的维度.
结论:
- 人工智能,ML和DL对于优化热电厂运营和生产率至关重要.
- 该审查强调了研究缺口,为未来的调查提供了方向.
- 进一步的研究被刺激,以探索能源领域的新型人工智能应用.
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