An Evaluation of Artificial Intelligence Integrated in Control Strategies in Building Services

Author:

Simtinică Marian-Cătălin1,Culcea Magdalena2,Caluianu Sorin3

Affiliation:

1. PhD Student, Technical University of Civil Engineering Bucharest , Romania

2. Research center CC-IELI, Technical University of Civil Engineering Bucharest , Romania

3. Prof. Dr. Eng., Research center CC-IELI, Technical University of Civil Engineering Bucharest , Romania

Abstract

Abstract This study carries out a literature review on artificial intelligence techniques used in building services for energy economy while maintaining the comfort of the occupants. The building services in which artificial intelligence techniques are used the most are: lighting systems, HVAC (heating, ventilation and air conditioning) systems, heating systems which use radiators and we also decided to include dynamic shading systems in here. The artificial intelligence techniques which are used the most in the recent years in building services are: fuzzy logic, artificial neural networks and for optimization problems, genetic algorithms are used. These techniques are utilized in many occasions to build predictive models or occupancy-based models.

Publisher

Walter de Gruyter GmbH

Reference36 articles.

1. [1] Directive 2018/844/EU of the European Parliament and of the Council of 30 May 2018 Amending Directive 2010/31/EU on the Energy Performance of Buildings and Directive 2012/27/EU on Energy Efficiency, Official Journal of the European Union 61, 19.6.2018, pp. 43–74. Accessible online at: http://data.europa.eu/eli/dir/2018/844/oj

2. [2] Minoli, D., Sohraby, K., & Occhiogrosso, B. (2017). IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems. IEEE Internet Of Things Journal, 4(1), 269-283. https://doi.org/10.1109/jiot.2017.264788110.1109/JIOT.2017.2647881

3. [3] Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, A., Duque-Pérez, O., & Santos García, F. (2021). A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis. Journal of Building Engineering, 33, 101692. https://doi.org/10.1016/j.jobe.2020.10169210.1016/j.jobe.2020.101692

4. [4] Malekpour Koupaei, D., Song, T., Cetin, K., & Im, J. (2020). An assessment of opinions and perceptions of smart thermostats using aspect-based sentiment analysis of online reviews. Building And Environment, 170, 106603. https://doi.org/10.1016/j.buildenv.2019.10660310.1016/j.buildenv.2019.106603

5. [5] Halhoul Merabet, G., Essaaidi, M., Ben Haddou, M., Qolomany, B., Qadir, J., & Anan, M. et al. (2021). Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques. Renewable and Sustainable Energy Reviews, 144, 110969. https://doi.org/10.1016/j.rser.2021.11096910.1016/j.rser.2021.110969

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3