楼宇自动化
建筑工程
可再生能源
零能耗建筑
高效能源利用
暖通空调
工作(物理)
能源消耗
计算机科学
工程类
环境经济学
机械工程
物理
电气工程
经济
空调
热力学
作者
Ali Bagheri,Konstantinos N. Genikomsakis,Sesil Koutra,Vasileios I. Sakellariou,Christos S. Ioakimidis
出处
期刊:Buildings
[MDPI AG]
日期:2021-12-05
卷期号:11 (12): 613-613
被引量:18
标识
DOI:10.3390/buildings11120613
摘要
Buildings’ heating and cooling systems account for an important part of total energy consumption. The EU’s directives and engagements motivate building owners and relevant stakeholders in the energy and construction sectors towards net zero energy buildings by maximizing the use of renewable energy sources, ICT, and automation systems. However, the high costs of investment for the renovation of buildings, in situ use of renewable energy production, and installation of expensive ICT infrastructure and automation systems in small–medium range buildings are the main obstacles for the wide adoption of EU building directives in small- and medium-range buildings. On the other hand, the concept of sharing computational and data storage resources among various buildings can be an alternative approach to achieving smart buildings and smart cities where the main control power resides on a server. Unlike other studies that focus on the implementation of AI techniques in a building or separated buildings with local processing resources and data storage, in this work a corporate server was employed to control the heating systems in three building typologies and to examine the potential benefits of controlling existing buildings in a unified energy-savings platform. The key finding of this work is that the AI algorithms incorporated into the proposed system achieved significant energy savings in the order of 20–40% regardless of building typology, building functionality, and type of heating system, despite the COVID-19 measures for frequent ventilation of the buildings, even in cases with older-type heating systems.
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