When artificial intelligence meets building energy efficiency, a review focusing on zero energy building
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When artificial intelligence meets building energy efficiency, a review focusing on zero energy building Biao Yan1 · Fei Hao2 · Xi Meng3
© Springer Nature B.V. 2020
Abstract 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. Keywords Artificial intelligence · Building energy efficiency · Thermal comfort · Zero energy building · Occupant behavior · Sensor and Internet of things Abbreviations HVAC Heating, ventilating and air-conditioning AI Artificial intelligence IoT Internet of things ZEB Zero energy building nZEB Nearly zero energy building * Biao Yan [email protected] 1
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
2
School of Computer Science, Shaanxi Normal University, Xi’an 710119, Shaanxi, China
3
Innovation Institute for Sustainable Maritime Architecture Research and Technology (iSMART), Qingdao University of Technology, Qingdao 266000, Shandong, China
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B. Yan et al.
UEB Ultra-low energy building ASHRAE American Society of Heating, Refrigerating, Air-Conditioning Engineers IES The Illuminating Engineering Society of North America USGBC US Green Building Council IECC International Energy Conservation Code ICC International Code Council ANN Artificial neural network GCHP Ground-coupled heat pump system PV Photovoltaic TLBO Teaching–learning-based optimization ABC Artificial bee colony TLABC Teaching-learning-based artificial bee colony MPPT Maximum power point tracking FL Fuzzy logic PID Proportional-Integral-Derivative PSO Particle Swarm Optimization PPD Predicted Percentage of Dissatisfied GA Genetic Algorithms SVR Support vect
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