Optimizing Occupant Comfort in a Room Using the Predictive Control Model as a Thermal Control Strategy

Author:

Boicu Mihaela-Gabriela1ORCID,Stamatescu Grigore2ORCID,Făgărăşan Ioana2ORCID,Vasluianu Mihaela1ORCID,Neculoiu Giorgian1ORCID,Dobrea Marius-Alexandru1

Affiliation:

1. Faculty of Hidrotechnics, Technical University of Civil Engineering Bucharest, 020396 Bucharest, Romania

2. Faculty of Automatic Control and Computer Science, The National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania

Abstract

Thermal comfort strategies represent a very important aspect when it comes to achieving thermal comfort conditions. At the same time, recently, there has been a growing interest in user-centered building control concepts. Thus, this work focuses on developing a thermal control strategy that combines the restrictions related to achieving thermal comfort, expressed in terms of environmental parameters and specific factors of personal perception, with the objective of reducing energy consumption. This case study aims at implementing this strategy in a laboratory room located within the Technical University of Civil Engineering Bucharest. The strategy proposed by the authors is based on implementing a combination of a Model Predictive Control (MPC) model and a fuzzy system, which presents constraints related to the room occupancy level. Relevant observations regarding the parameterization of fuzzy systems are also highlighted.

Funder

National Research Grant of the UTCB

Publisher

MDPI AG

Reference29 articles.

1. Review of practices for human thermal comfort in buildings: Present and future perspectives;Sansaniwal;Int. J. Ambient. Energy,2020

2. Grassi, B., Piana, E.A., Lezzi, A.M., and Pilotelli, M. (2022). A Review of Recent Literature on Systems and Methods for the Control of Thermal Comfort in Buildings. Appl. Sci., 12.

3. Bueno, A.M., Xavier, A.A.d.P., and Broday, E.E. (2021). Evaluating the Connection between Thermal Comfort and Productivity in Buildings: A Systematic Literature Review. Buildings, 11.

4. Temperature-preference learning with neural networks for occupant-centric building indoor climate controls;Peng;J. Affect. Disord.,2019

5. Design and implementation of an occupant-centered self-learning controller for decentralized residential ventilation systems;Carbonare;J. Affect. Disord.,2021

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