A SCIENCE MAPPING APPROACH BASED REVIEW OF MODEL PREDICTIVE CONTROL FOR SMART BUILDING OPERATION MANAGEMENT

被引：8
作者：
Wang, Jun [1 ]
Chen, Jianli [2 ]
Hu, Yuqing [3 ]
机构：
[1] Qingdao Univ Technol, Sch Management Engn, Qingdao, Peoples R China
[2] Univ Utah, Dept Civil Engn, Salt Lake City, UT 84112 USA
[3] Penn State Univ, Dept Architectural Engn, University Pk, PA 16802 USA
来源：
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT | 2022年 / 28卷 / 08期
关键词：
model predictive control (MPC); building operation management; science mapping; literature review; THERMAL COMFORT; ENERGY MANAGEMENT; NEURAL-NETWORK; DEMAND-RESPONSE; COMMERCIAL BUILDINGS; HVAC CONTROL; MULTIOBJECTIVE OPTIMIZATION; CONTROL FRAMEWORK; HEATING-SYSTEMS; IDENTIFICATION;
D O I：
10.3846/jcem.2022.17566
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Model predictive control (MPC) for smart building operation management has become an increasingly popular and important topic in the academic community. Based on a total of 202 journal articles extracted from Web of Science, this study adopted a science mapping approach to conduct a holistic review of the literature sample. Chronological trends, contributive journal sources, active scholars, influential documents, and frequent keywords of the literature sample were identified and analyzed using science mapping. Qualitative discussions were also conducted explore in details the objectives and data requirements of MPC implementation, different modeling approaches, common optimization methods, and associated model constraints. Three research gaps and future directions of MPC were presented: the selection and establishment of MPC central model, the capability and security of processing massive data, and the involvement of human factors. This study provides a big picture of existing research on MPC for smart building operations and presents findings that can serve as comprehensive guides for researchers and practitioners to connect current research with future trends.
引用
收藏
页码：661 / 679
页数：19
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