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

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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.

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Abbreviations

HVAC:

Heating, ventilating and air-conditioning

AI:

Artificial intelligence

IoT:

Internet of things

ZEB:

Zero energy building

nZEB:

Nearly zero energy building

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 vector regression

SVM:

Support vector machine

CRT:

Classification and regression tree

GLR:

General linear regression

CAID:

Chi-squared automatic interaction detector

EIM:

Ensemble inference model

SI:

Swarm intelligence

PLS:

Partial least squares regress

WT:

Wavelet transform

FDD:

Fault detection and diagnosis

VIC:

Virtual in situ sensor calibration

MCMC:

Markov chain Monte Carlo

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Acknowledgements

This work was partially supported by the Project of Guangdong University of Technology (2018-148/134, Exploration Study of Talents Fostering in Building Environment and Energy Engineering Based on AI Techniques).

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  1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China

    Biao Yan

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, Shaanxi, China

    Fei Hao

  3. Innovation Institute for Sustainable Maritime Architecture Research and Technology (iSMART), Qingdao University of Technology, Qingdao, 266000, Shandong, China

    Xi Meng

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Yan, B., Hao, F. & Meng, X. When artificial intelligence meets building energy efficiency, a review focusing on zero energy building. Artif Intell Rev 54, 2193–2220 (2021). https://doi.org/10.1007/s10462-020-09902-w

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