With the rapid development of ground source heat pump (GSHP) system, energy saving measures are of special interest for practice. In order to meet heating demand, capacity control of GSHP system can be carried out by regulating either part load ratio (PLR) or supply water temperature. A data-driven optimization approach was developed and applied on a school building in heating mode, which aims at minimizing energy consumption without compromising thermal comfort. An artificial neural network (ANN) model of the GSHP system was proposed and trained with experimental data as well as simulated data of a validated physics-based model, which was employed for data supplement to cover more data variations. The multi-objective optimization problem was then solved using genetic algorithm. The results suggest the optimal operation strategy for either continuous or staged capacity control regarding heating demand variation. With the proposed optimal control strategy, energy savings as compared to existing strategy can be up to 22% for a single month and 14% for the whole heating season.

In northern China, radiant floor heating is widely used in multi-apartment residential buildings, with indoor temperature being a key factor in evaluating a user’s heating demands. However, due to variations in building structure, room orientation, and the outdoor environment, identifying the optimal placement of temperature sensors across multiple zones remains challenging. In this study, we propose a data-driven methodology to identify the optimal placement of temperature sensors for a typical apartment with multiple zones. The proposed methodology is based on computational fluid dynamics (CFD) simulations of several typical scenarios and quantifies the relationship between the temperature field and the volume-averaged operating temperature to determine the optimal locations for temperature sensors. Results indicate that the temperature sensors need to be placed on planes ranging from 1.0 m to 1.7 m, with each plane featuring a distinct optimal area. The RMSE analysis reveals that, despite obvious temperature variations across the residence, the root mean square errors (RMSEs) at the designated sensor locations remain consistently low, with a maximum of 0.35 °C and most values below 0.3 °C. The above results indicate that the optimal sensor placement can significantly reduce potential errors between recorded temperatures and volume-averaged operating temperatures, which can be used as input parameters for personal indoor temperature control.

Recent years, more and more utility tunnels are adopted in China with the development of urbanization. Compared to the conventional method for utility installation, the utility tunnel that co-locates more than one utility underground eliminates the need for frequent excavation and reinstatement of roads. Ventilation systems are essential in utility tunnel for safety reasons, especially for gas compartments. This paper aims to evaluate effect of the longitudinal ventilation on gas temperature field by investigating the heat transfer process on gas pipeline. Numerical models including convection, conduction and radiation were established and then used to simulate the heat transfer process on gas pipeline. Results show that the ventilation has a great impact on gas temperature. In case of low inlet air temperature, the gas temperature may drop below -10 °C, which is too low to guarantee a normal operation.