Analysis of the building occupancy estimation and prediction process: A systematic review

被引:4
作者
Caballero-Pena, Juan [1 ,2 ]
Osma-Pinto, German [1 ]
Rey, Juan M. [1 ]
Nagarsheth, Shaival [2 ]
Henao, Nilson [2 ]
Agbossou, Kodjo [2 ]
机构
[1] Univ Ind Santander, Escuela Ingn Elect Elect & Telecomunicac, Grp Invest GISEL, Bucaramanga 680002, Colombia
[2] Univ Quebec Trois Rivieres, Dept Genie Elect & Genie Informat, Lab Innovat & Rech Energie Intelligente LIREI, Trois Rivieres, PQ G9A 5H7, Canada
关键词
Building occupancy; Feature selection; Data-driven methods; Evaluation metrics; Model predictive control; THERMAL COMFORT; DIGITAL TWINS; ENERGY; MODEL; BEHAVIOR; MANAGEMENT; SIMULATION;
D O I
10.1016/j.enbuild.2024.114230
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The prediction of the occupancy in buildings is essential to design efficient energy control strategies that optimize consumption and reduce losses while guaranteeing the comfort of the occupants. For this reason, many works address the problem of detecting, estimating, and predicting buildings' occupancy using different techniques, devices, and technologies. The occupancy prediction process can be described in four stages: data acquisition, modeling, evaluation, and testing, which are closely related. This paper reviews the most relevant recent literature on building occupancy estimation and prediction, analyzing the key aspects of its stages. A detailed description of the variables and design considerations is presented, including measurement methods, sensor selection, modeling techniques, evaluation metrics, and different applications. Through its examination, this paper elaborates significant remarks on the interaction between the stages, providing an overview of the suitable design of the occupancy prediction process. Finally, current and future trends are discussed.
引用
收藏
页数:16
相关论文
共 126 条
[21]   Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data [J].
Chang, Wen-Kuei ;
Hong, Tianzhen .
BUILDING SIMULATION, 2013, 6 (01) :23-32
[22]   Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study [J].
Chen, Xiao ;
Cao, Benyi ;
Pouramini, Somayeh .
ENERGY, 2023, 270
[23]   Simulation and visualization of energy-related occupant behavior in office buildings [J].
Chen, Yixing ;
Liang, Xin ;
Hong, Tianzhen ;
Luo, Xuan .
BUILDING SIMULATION, 2017, 10 (06) :785-798
[24]   Building occupancy modeling using generative adversarial network [J].
Chen, Zhenghua ;
Jiang, Chaoyang .
ENERGY AND BUILDINGS, 2018, 174 :372-379
[25]   Building occupancy estimation and detection: A review [J].
Chen, Zhenghua ;
Jiang, Chaoyang ;
Xie, Lihua .
ENERGY AND BUILDINGS, 2018, 169 :260-270
[26]   Modeling regular occupancy in commercial buildings using stochastic models [J].
Chen, Zhenghua ;
Xu, Jinming ;
Soh, Yeng Chai .
ENERGY AND BUILDINGS, 2015, 103 :216-223
[27]   Occupancy estimation with environmental sensors: The possibilities and limitations [J].
Chitnis S. ;
Somu N. ;
Kowli A. .
Energy and Built Environment, 2025, 6 (01) :96-108
[28]   Occupant-centric urban building energy modeling: Approaches, inputs, and data sources-A review [J].
Dabirian, Sanam ;
Panchabikesan, Karthik ;
Eicker, Ursula .
ENERGY AND BUILDINGS, 2022, 257
[29]   PRECEPT: Occupancy Presence Prediction Inside A Commercial Building [J].
Das, Anooshmita ;
Kjaergaard, Mikkel Baun .
UBICOMP/ISWC'19 ADJUNCT: PROCEEDINGS OF THE 2019 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2019 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS, 2019, :486-491
[30]   Reinforcement learning of occupant behavior model for cross-building transfer learning to various HVAC control systems [J].
Deng, Zhipeng ;
Chen, Qingyan .
ENERGY AND BUILDINGS, 2021, 238