Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optimisations: A state-of-the-art review

• Published in: Renewable & sustainable energy reviews Vol. 130; p. 109889
• Main Authors: Zhou, Yuekuan, Zheng, Siqian, Liu, Zhengxuan, Wen, Tao, Ding, Zhixiong, Yan, Jun, Zhang, Guoqiang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2020
• Subjects: Combined active and passive energy systems; Exhaust heat recovery; Machine learning; Multivariable and multi-objective optimisations; Phase change materials (PCMs); Stochastic uncertainty-based prediction; Exhaust heat recovery; Phase change materials (PCMs); Stochastic uncertainty-based prediction; Multivariable and multi-objective optimisations; Machine learning; Combined active and passive energy systems
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2020.109889

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. •Systematic and comprehensive review of PCMs in active and passive energy systems.•Heat-transfer mechanism, modelling and dialectical analysis of feasibility.•Machine-learning based multivariable and multi-objective optimisations.•PCM integrated exhaust heat recovery systems for thermal energy and electricity management.•An interactive thermal energy network for flexible energy interactions in smart city.