A review of research on intelligent technology in building air conditioning system optimisation.

The Sustainable Development Goals (SDGs) aim to enhance cities and communities more comfortable, safe, and environmentally friendly. The SDGs state that the construction sector should take a low-carbon and sustainable development path. During the operation phase of buildings, the energy consumed by air conditioning systems makes up approximately 22 % of the overall energy consumption of a building. The optimisation of the air conditioning operation control strategy is significant for saving energy and cutting emissions. However, a building air conditioning system is a complex system with multiple parameters, nonlinearity, time variance, and multiple objective values. Traditional air conditioning control methods cannot meet the complex needs of energy saving and comfort improvement in a dynamic environment. Currently, many researchers are studying intelligent technologies for optimising building air conditioning systems. This literature review categorises the relevant articles in this field in recent years, discusses the aspects of optimisation of control methods and the application of intelligent technologies, and focuses on the analysis of the characteristics of various intelligent technologies such as Model Predictive Control, Machine Learning, Deep Learning, and intelligent optimisation algorithms, and their advantages in the domain of optimising control for building air conditioning systems.