Stochastic model predictive control for the optimal operation of office buildings

IF 7.1 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Building and Environment Pub Date : 2024-10-30 DOI:10.1016/j.buildenv.2024.112248
Ning He , Jiawen Guo , Yanxin Li , Yubo Quan , Ruoxia Li , Liu Yang
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Abstract

This paper developed a novel stochastic model predictive control (SMPC) strategy to enhance the operational efficiency of office buildings. Firstly, an improved state space model encompassing temperature and relative humidity simultaneously is developed to accurately characterize the thermal comfort condition within the office building. Then, given the obtained comprehensive model, a new SMPC approach is proposed based on chance constraints to minimize energy consumption while guaranteeing thermal comfort for occupants. Besides, the feasibility and stability properties of the SMPC are demonstrated theoretically. Finally, the proposed SMPC method is verified through a real office building located in Xi'an, China, and the result shows that compared to the conventional ONOFF and MPC control strategies, the SMPC can achieve 39.1 % and 33.3 % energy-saving and less temperature and relative humidity requirement violations.
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办公楼优化运行的随机模型预测控制
本文开发了一种新颖的随机模型预测控制(SMPC)策略,以提高办公楼的运行效率。首先,建立了一个同时包含温度和相对湿度的改进状态空间模型,以准确描述办公楼内的热舒适状况。然后,根据所获得的综合模型,提出了一种基于机会约束的新的 SMPC 方法,以在保证居住者热舒适度的同时最大限度地降低能耗。此外,还从理论上论证了 SMPC 的可行性和稳定性。最后,通过位于中国西安的一栋真实办公楼对所提出的 SMPC 方法进行了验证,结果表明,与传统的 ONOFF 和 MPC 控制策略相比,SMPC 可分别实现 39.1% 和 33.3% 的节能效果,且较少违反温度和相对湿度要求。
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来源期刊
Building and Environment
Building and Environment 工程技术-工程:环境
CiteScore
12.50
自引率
23.00%
发文量
1130
审稿时长
27 days
期刊介绍: Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.
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