From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices

被引:20
作者
Shahcheraghian, Amir [1 ]
Madani, Hatef [2 ]
Ilinca, Adrian [1 ]
机构
[1] Ecole Technol Super, Dept Mech Engn, Montreal, PQ H3C 1K3, Canada
[2] KTH Royal Inst Technol, Dept Energy Technol, Stockholm, Sweden
基金
英国科研创新办公室;
关键词
BES; simulation tool; white-box; black-box; machine learning; deep learning; building energy; NEURAL-NETWORKS; PERFORMANCE; PREDICTION; LOAD; CONSUMPTION; EFFICIENCY;
D O I
10.3390/en17020376
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Buildings consume significant energy worldwide and account for a substantial proportion of greenhouse gas emissions. Therefore, building energy management has become critical with the increasing demand for sustainable buildings and energy-efficient systems. Simulation tools have become crucial in assessing the effectiveness of buildings and their energy systems, and they are widely used in building energy management. These simulation tools can be categorized into white-box and black-box models based on the level of detail and transparency of the model's inputs and outputs. This review publication comprehensively analyzes the white-box, black-box, and web tool models for building energy simulation tools. We also examine the different simulation scales, ranging from single-family homes to districts and cities, and the various modelling approaches, such as steady-state, quasi-steady-state, and dynamic. This review aims to pinpoint the advantages and drawbacks of various simulation tools, offering guidance for upcoming research in the field of building energy management. We aim to help researchers, building designers, and engineers better understand the available simulation tools and make informed decisions when selecting and using them.
引用
收藏
页数:45
相关论文
共 163 条
[41]  
Building Performance Database (BPD), about us
[42]  
Buildsim, about us
[43]   Recent Advances in Sustainable Energy and Environmental Development [J].
Calise, Francesco ;
Figaj, Rafal .
ENERGIES, 2022, 15 (18)
[44]  
Chen YZ, 2017, CONF REC ASILOMAR C, P1368, DOI 10.1109/ACSSC.2017.8335578
[45]   Physical energy and data-driven models in building energy prediction: A review [J].
Chen, Yongbao ;
Guo, Mingyue ;
Chen, Zhisen ;
Chen, Zhe ;
Ji, Ying .
ENERGY REPORTS, 2022, 8 :2656-2671
[46]   Building energy performance forecasting: A multiple linear regression approach [J].
Ciulla, G. ;
D'Amico, A. .
APPLIED ENERGY, 2019, 253
[47]  
Claudio R., 2020, Masters Thesis
[48]  
ClimaPlus, about us
[49]   EnergyPlus: creating a new-generation building energy simulation program [J].
Crawley, DB ;
Lawrie, LK ;
Winkelmann, FC ;
Buhl, WF ;
Huang, YJ ;
Pedersen, CO ;
Strand, RK ;
Liesen, RJ ;
Fisher, DE ;
Witte, MJ ;
Glazer, J .
ENERGY AND BUILDINGS, 2001, 33 (04) :319-331
[50]   Contrasting the capabilities of building energy performance simulation programs [J].
Crawley, Drury B. ;
Hand, Jon W. ;
Kurnmert, Michal ;
Griffith, Brent T. .
BUILDING AND ENVIRONMENT, 2008, 43 (04) :661-673