Taking the bed in the bedroom as an air-conditioning system (ACS), this paper attempts to disclose the influence of air supply height (ASH) on sleep comfort. Specifically, a computational fluid dynamics (CFD) model was established to simulate the air temperature distribution, air speed, and carbon dioxide content (CDC) indoor at five ASHs of the ACS. The simulation results were compared with lab data. On this basis, the aeration efficiency (AE), temperature regulation risk (TRR), and energy-use factor (EUF) of the ACS were analyzed under different working conditions, and the overall efficacy of the ACS was assessed comprehensively by the technique for order of preference by similarity to ideal solution (TOPSIS).

To figure out the operating features of Direct-Expansion Air-Conditioning (DX-AC) system, this paper employed the BP training algorithm and built a two-in/two-out steady-state model based on artificial neural network (ANN) and connect the DX-AC system’s Totally output Cooling Capacity (TCC) and Sensible Heat Ratio (SHR) under different combinations of rotational velocities of compressor and blower; then, the paper verified the proposed model by comparing the prediction results of the proposed model with experimental data, and the numerical analysis of the steady-state model and bilinear interpolation indicated that the ANN-based model proposed in the paper showed a high prediction accuracy and can well exhibit its operating performance.