Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques

04/06/2021
by   Ghezlane Halhoul Merabet, et al.
7

Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while maintaining comfortable conditions in buildings are conflicting objectives and represent a typical optimization problem that requires intelligent system design. Over the last decade, different methodologies based on the Artificial Intelligence (AI) techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels to the occupants. This paper performs a comprehensive and an in-depth systematic review of AI-based techniques used for building control systems by assessing the outputs of these techniques, and their implementations in the reviewed works, as well as investigating their abilities to improve the energy-efficiency, while maintaining thermal comfort conditions. This enables a holistic view of (1) the complexities of delivering thermal comfort to users inside buildings in an energy-efficient way, and (2) the associated bibliographic material to assist researchers and experts in the field in tackling such a challenge. Among the 20 AI tools developed for both energy consumption and comfort control, functions such as identification and recognition patterns, optimization, predictive control. Based on the findings of this work, the application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory. This is mainly due in part to the fact that these algorithms usually need a large amount of high-quality real-world data, which is lacking in the building or, more precisely, the energy sector.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 28

page 29

page 32

page 33

page 34

page 36

page 41

10/26/2021

Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control

It is estimated that about 40 commercial buildings can be attributed to ...
04/16/2019

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in ...
08/08/2017

ThermalSim: A Thermal Simulator for Error Analysis

Researchers have extensively explored predictive control strategies for ...
10/09/2020

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and s...
06/19/2020

End-to-end deep metamodeling to calibrate and optimize energy loads

In this paper, we propose a new end-to-end methodology to optimize the e...
07/20/2020

Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards

Computing systems have undergone several inflexion points - while Moore'...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.