Phase change materials (PCMs) with considerable energy storage density show promise for building cooling performance enhancements, but the cooling performance is highly dependent on novel PCM-integrated forms and intelligent control strategies. This chapter provides a state-of-the-art review of novel PCM-based strategies for building cooling performance enhancements. The strategies investigated include PCM-integrated forms (such as distributed and coupled systems) and combined strategies (such as high-reflective coatings, radiative cooling walls, and hybrid ventilation). Heat transfer mechanisms for both distributed and coupled systems have been characterized to provide an in-depth understanding. Solutions for system performance enhancement of novel PCM-based cooling systems have been comprehensively presented. Future studies and prospects have been demonstrated as avenues for future research. This study presents a systematic overview of novel PCM-based strategies for cooling performance enhancement, together with technical challenges of the widespread applications. Research results are critical for the application and promotion of novel PCM-based cooling strategies in buildings.@en

Integrating phase change materials (PCMs) in buildings cannot only enhance the energy performance, but also improve the renewable utilization efficiency through considerable latent heat during charging/discharging cycles. However, system performances are dependent on PCMs’ integrated forms, heat transfer enhancement solutions, system operating modes, together with optimal geometrical and operating parameters. In this study, passive, active, and combined passive/active solutions in PCMs systems have been comprehensively reviewed, when being applied in heating, cooling and electrical systems, together with a dialectical analysis on advantages and disadvantages. In addition to novel system designs, interdisciplinary applications of machine learning have been reviewed and formulated, from perspectives of reliable structures, smart operational controls, and stochastic uncertainty-based performance prediction. Furthermore, a generic methodology with a systematic and hierarchical procedure has been proposed, with the implementation of machine-learning based technique for optimisations during both design and operation periods. The mechanisms of machine learning techniques were characterised as the simplifications of modelling and optimization processes, through the errors-driven update, the support vector regression and the backpropagation neural network. Several technical challenges were identified, such as the heat transfer enhancement, the novel structural configurations and the flexible switch on operating modes. Finally, identified challenges on machine learning include the development of advanced learning algorithms for efficient performance predictions, optimal structural configurations on neural networks, the trade-off between computational complexity and reliable optimal solutions, and so on. The formulated climate-adaptive designs, intelligent operations, uncertainty-based analysis and optimisations with interdisciplinary machine learning techniques can promote PCMs applications in sustainable buildings.@en