A review on cooling performance enhancement for phase change materials integrated systems—flexible design and smart control with machine learning applications

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

7 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Article number106786
Journal / PublicationBuilding and Environment
Volume174
Online published6 Mar 2020
Publication statusPublished - May 2020

Abstract

Climate-adaptive design, smart control, latent thermal storages, multi-dimensional uncertainty analysis, and multi-objective optimisations are effective solutions for cooling performance enhancement of buildings through integrated techniques, such as hybrid ventilations, nocturnal sky radiation, radiative cooling and active PV cooling for the self-consumption. However, there is no systematic and in-depth analysis on this topic in the academia. In this study, a state-of-the-art review on novel PCMs based strategies to reduce cooling load of buildings has been presented. The investigated strategies include the structural configuration, systematic control and the multi-criteria for assessment. The roles of ventilations, radiative cooling and the underlying heat transfer mechanism have been characterized for the in-depth understanding. In order to realise the multivariable optimal design and robust operations under multi-level scenario uncertainties, parametric and uncertainty analysis, single- and multi-objective optimisations have been comprehensively reviewed, together with technical challenges for each solution. Research results show that, integrated passive and active systems with flexible transitions on operating modes are full of prospects for the multi-criteria performance improvement. Trade-off solutions along the multi-objective Pareto frontier are multi-diversified, dependent on the adopted approach and the studied scenario. Furthermore, machine learning methods are promising for the thermal and energy performances improvement, through the surrogate model development, the model predictive control and the optimisation function. Future studies and prospects have been demonstrated as avenues for future research. This study presents a systematic overview on novel PCMs based strategies, together with the application of machine-learning methods for cooling performance enhancement, which are critical for the promotion of novel PCMs based cooling strategies in buildings.

Research Area(s)

  • Hybrid ventilations, Machine learning, Parametric and uncertainty analysis, Phase change materials, Radiative cooling, Single and multi-objective optimisation

Citation Format(s)