When artificial intelligence meets building energy efficiency, a review focusing on zero energy building

被引:49
作者
Yan, Biao [1 ]
Hao, Fei [2 ]
Meng, Xi [3 ]
机构
[1] Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Guangdong, Peoples R China
[2] Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Shaanxi, Peoples R China
[3] Qingdao Univ Technol, Innovat Inst Sustainable Maritime Architecture Re, Qingdao 266000, Shandong, Peoples R China
关键词
Artificial intelligence; Building energy efficiency; Thermal comfort; Zero energy building; Occupant behavior; Sensor and Internet of things; NEURAL-NETWORKS; BIG DATA; MULTIOBJECTIVE OPTIMIZATION; OCCUPANT BEHAVIOR; HEATING-SYSTEM; COOLING LOADS; BEE COLONY; PERFORMANCE; CONSUMPTION; PREDICTION;
D O I
10.1007/s10462-020-09902-w
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Building energy efficiency, as a traditional field which has been existing for decades performs a prosperous needs with diversity of corresponding methods. In the flow of artificial intelligence (AI) background, where does the building energy efficiency advance and how does it emphasize? This question seems to become more significant with the blueprints of zero energy building implementation issued by many countries. The major objective of this research is to review, analyze and identify the performance of AI based applications in buildings, especially for building energy efficiency and zero energy building. Based on the present research trends, the possible changes AI based approach brings to related laws, regulations and standards are firstly analyzed. The main aspects of the AI based approach infrastructure in buildings is thoroughly reviewed and compared. IoT based sensor applications for thermal comfort, platforms and algorithms for building multi energies control, and forecasting methods for building load, subsystem performance and structure safety are summarized. To provide more optimal references for zero energy building solutions, the AI based approach in zero energy building is then predicted in detail, with particular analysis of occupant presence and behaviors. Finally, the future directions of the research on AI based applications for zero energy building implementation are summarized.
引用
收藏
页码:2193 / 2220
页数:28
相关论文
共 112 条
[91]   Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies [J].
Sovacool, Benjamin K. ;
Del Rio, Dylan D. Furszyfer .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2020, 120
[92]   Accelerating the discovery of materials for clean energy in the era of smart automation [J].
Tabor, Daniel P. ;
Roch, Loic M. ;
Saikin, Semion K. ;
Kreisbeck, Christoph ;
Sheberla, Dennis ;
Montoya, Joseph H. ;
Dwaraknath, Shyam ;
Aykol, Muratahan ;
Ortiz, Carlos ;
Tribukait, Hermann ;
Amador-Bedolla, Carlos ;
Brabec, Christoph J. ;
Maruyama, Benji ;
Persson, Kristin A. ;
Aspuru-Guzik, Alan .
NATURE REVIEWS MATERIALS, 2018, 3 (05) :5-20
[93]  
Theraulaz G, 1999, INFORMATION PROCESSING IN SOCIAL INSECTS, P309
[94]  
Torcellini PA, 2006, ASHRAE J, V48, P62
[95]   Applying artificial intelligence modeling to optimize green roof irrigation [J].
Tsang, S. W. ;
Jim, C. Y. .
ENERGY AND BUILDINGS, 2016, 127 :360-369
[96]   Cooperative energy management of a community of smart-buildings: A Blockchain approach [J].
Van Cutsem, Olivier ;
Dac, David Ho ;
Boudou, Pol ;
Kayal, Maher .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 117
[97]   Climate responsive cooling control using artificial neural networks [J].
Venkatesan, K. ;
Ramachandraiah, U. .
JOURNAL OF BUILDING ENGINEERING, 2018, 19 :191-204
[98]   FROM LOW-ENERGY TO NET ZERO-ENERGY BUILDINGS: STATUS AND PERSPECTIVES [J].
Voss, Karsten ;
Musall, Eike ;
Lichtmess, Markus .
JOURNAL OF GREEN BUILDING, 2011, 6 (01) :46-57
[99]   Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach [J].
Wang, Lan ;
Lee, Eric W. M. ;
Yuen, Richard K. K. .
APPLIED ENERGY, 2018, 228 :1740-1753
[100]   Distributed aggregation control of grid-interactive smart buildings for power system frequency support [J].
Wang, Yu ;
Xu, Yan ;
Tang, Yi .
APPLIED ENERGY, 2019, 251