人工智能驱动的智能楼宇能源管理系统。

Q1 Engineering

电网技术 Pub Date : 2024-04-13 DOI:10.52783/pst.280

Balakumar Muniandi, Purushottam Kumar Maurya, CH Bhavani, Shailesh Kulkarni, Ramswaroop Reddy Yellu, Nidhi C

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引用次数: 0

摘要

人工智能（AI）的出现彻底改变了智能建筑的能源管理格局，为优化能源消耗、提高运营效率和推进可持续发展目标提供了无与伦比的机遇。本文全面回顾了为智能建筑量身定制的人工智能驱动型能源管理系统，探讨了其多方面的功能、优势、挑战和未来前景。[1]、[4] 通过综合现有文献和案例研究，本研究旨在阐明人工智能在重塑建筑环境能源管理和利用方式方面的变革潜力。人工智能驱动的能源管理系统利用先进的算法、机器学习技术和数据分析，对智能建筑内的能源使用情况进行智能监控、分析和优化。这些系统集成了各种组件，如传感设备、数据预处理模块、优化算法和控制系统，以实现最佳性能。主要功能包括预测分析能源需求，自适应控制供暖、通风和空调（HVAC）系统，基于占用模式的动态照明管理，以及集成可再生能源以提高可持续性。人工智能使智能楼宇能够参与需求响应计划，根据电网状况和价格信号动态调整能源消耗。这种灵活性不仅可以降低运营成本，还有助于提高电网的稳定性和恢复能力。然而，广泛采用人工智能驱动的能源管理系统面临着一些挑战，包括数据隐私问题、互操作性问题，以及需要技术熟练的人员来操作和维护这些复杂的系统。本文强调了人工智能驱动的能源管理系统作为智能建筑时代优化能源利用、提高建筑性能和推进可持续发展目标的变革性工具的重要性。DOI: https://doi.org/10.52783/pst.280

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AI-Driven Energy Management Systems for Smart Buildings.

The advent of Artificial Intelligence (AI) has revolutionized the energy management landscape for smart buildings, offering unparalleled opportunities for optimizing energy consumption, enhancing operational efficiency, and advancing sustainability goals. This paper provides a comprehensive review of AI-driven energy management systems tailored for smart buildings, exploring their multifaceted functionalities, benefits, challenges, and future prospects. [1],[4] By synthesizing existing literature and case studies, this research aims to elucidate the transformative potential of AI in reshaping the way energy is managed and utilized in the built environment. AI-driven energy management systems leverage advanced algorithms, machine learning techniques, and data analytics to intelligently monitor, analyze, and optimize energy usage within smart buildings. These systems integrate diverse components such as sensing devices, data preprocessing modules, optimization algorithms, and control systems to achieve optimal performance. Key functionalities include predictive analytics for energy demand forecasting, adaptive control of heating, ventilation, and air conditioning (HVAC) systems, dynamic lighting management based on occupancy patterns, and integration with renewable energy sources to enhance sustainability. AI enables smart buildings to participate in demand response programs, dynamically adjusting energy consumption in response to grid conditions and pricing signals. This flexibility not only reduces operational costs but also contributes to grid stability and resilience. However, the widespread adoption of AI-driven energy management systems faces several challenges, including data privacy concerns, interoperability issues, and the need for skilled personnel to operate and maintain these sophisticated systems.The paper underscores the importance of AI-driven energy management systems as transformative tools for optimizing energy utilization, improving building performance, and advancing sustainability objectives in the era of smart buildings. DOI: https://doi.org/10.52783/pst.280

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来源期刊

电网技术 Engineering-Mechanical Engineering

CiteScore

7.30

自引率

0.00%

发文量

13735

期刊介绍： "Power System Technology" (monthly) was founded in 1957. It is a comprehensive academic journal in the field of energy and power, supervised and sponsored by the State Grid Corporation of China. It is published by the Power System Technology Magazine Co., Ltd. of the China Electric Power Research Institute. It is publicly distributed at home and abroad and is included in 12 famous domestic and foreign literature databases such as the Engineering Index (EI) and the National Chinese Core Journals. The purpose of "Power System Technology" is to serve the national innovation-driven development strategy, promote scientific and technological progress in my country's energy and power fields, and promote the application of new technologies and new products. "Power System Technology" has adhered to the publishing characteristics of combining "theoretical innovation with applied practice" for many years, and the scope of manuscript selection covers the fields of power generation, transmission, distribution, and electricity consumption.