Recent years have seen increasing attention to TCES technology owing to its potentially high energy density and suitability for long-duration storage with negligible loss, and it benefits the deployment of future net-zero energy systems. This paper provides a review of salt hydrate adsorption material-based TCES for space heating applications at ~150 °C. The incorporation of salt hydrates into a porous matrix to form composite materials provides the best avenue to overcome some challenges such as mass transport limitation and lower thermal conductivity. Therefore, a systematic classification of the host matrix is given, and the most promising host matrix, MIL-101(Cr)(MOFs), which is especially suitable for loading hygroscopic salt, is screened from the perspective of hydrothermal stability, mechanical strength, and water uptake. Higher salt content clogs pores and, conversely, reduces adsorption performance; thus, a balance between salt content and adsorption/desorption performance should be sought. MgCl₂/rGOA is obtained with the highest salt loading of 97.3 wt.%, and the optimal adsorption capacity and energy density of 1.6 g·g⁻¹ and 2225.71 kJ·kg⁻¹, respectively. In general, larger pores approximately 8–10 nm inside the matrix are more favorable for salt dispersion. However, for some salts (MgSO₄-based composites), a host matrix with smaller pores (2–3 nm) is beneficial for faster reaction kinetics. Water molecule migration behavior, and the phase transition path on the surface or interior of the composite particles, should be identified in the future. Moreover, it is essential to construct a micromechanical experimental model of the interface.

The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network‐based fuzzy inference system (ANFIS) model and improved particle swarm optimization (PSO) algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input‐output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.