使用基于人工智能的机器学习模型进行资产管理的综述:电力和能源系统的应用

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Gopal Lal Rajora, Miguel A. Sanz-Bobi, Lina Bertling Tjernberg, José Eduardo Urrea Cabus
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引用次数: 0

摘要

电力系统保护和资产管理是长期存在的技术挑战,尤其是在智能电网和可再生能源领域。本文旨在全面评估机器学习在电力系统有效资产管理中的应用,从而应对这些挑战。研究的重点是在保持环境可持续性和效率的同时,能源生产需求的不断增长。通过利用人工智能(AI)、机器学习(ML)和深度学习(DL)等现代技术的力量,本研究探讨了如何利用 ML 技术作为电力行业的强大工具。通过展示实际应用和成功案例,本文证明了机器学习作为满足电力行业当前和未来业务需求的一项重要技术正被越来越多的人所接受。此外,本研究还探讨了在实际环境中大规模部署 ML 的障碍和困难,同时探索了这些策略的潜在机遇。通过这一概述,我们可以深入了解 ML 在塑造未来电力系统资产管理方面的变革潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A review of asset management using artificial intelligence-based machine learning models: Applications for the electric power and energy system

A review of asset management using artificial intelligence-based machine learning models: Applications for the electric power and energy system

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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