## 5 Conclusions  
In this study, a medium-depth GSHP system is fabricated for an office building in Xi'an, in which the particle swarm algorithm is improved by adjusting the parameters of the algorithm and introducing the chaotic optimization idea. Based on the improved particle swarm optimization algorithm, the system operation is optimized with the operating costs, system COP, and the geothermal energy utilization coefficient as the objective functions, respectively, while the optimization results are compared and analyzed. The main conclusions are as follows:  
1. Taking the operating costs as the objective function, the buried pipe flow rate first decreases from approximately 14 m\({}^{3}\)/h to 8.5 m\({}^{3}\)/h and then increases to 11 m\({}^{3}\)/h with 18:00 as the demarcation point, and the higher power consumption of the system in 21:00-10:00, about 25-30 kW. At the same time, the working conditions of the heat storage tank are mainly divided into three stages, 2:00-10:00, 20:00-24:00, which is the heat release mode, and 12:00-20:00, which is the heat storage mode. The heat storage capacity first changes from approximately 300 kW to 0 in 2:00-10:00, then from 0 to 150 kW in 20:00-24:00, and then to 0 in 12:00-20:00. Furthermore, the system transfers some of the power consumption from the peak power period to the valley power period by switching the heat storage and heat release modes of the heat storage tank. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objective** & **Operating Costs** & **System** & **Geothermal Energy Utilization** \\
**Functions** & **(CNY)** & **COP** & **Coefficient** \\ \hline Operating costs & 279.27 & 5.8257 & 0.8369 \\ System COP & 300.02 & 6.4420 & 0.8524 \\ Geothermal energy & 300.74 & 6.4413 & 0.8527 \\ Utilization coefficient & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation indicators of the system after optimization with different objective functions. Figure 14: Electricity consumption in each period when the objective function is geothermal energy utilization coefficient. In this way, the operating cost is the lowest, CNY 279.27. The system COP and the geothermal energy utilization coefficient are 5.8257 and 0.8369, respectively. 2. Taking the system COP as the objective function, the buried pipe flow rate fluctuates slightly at a high level, with a maximum of 14 m\({}^{3}\)/h and a minimum of 11.75 m\({}^{3}\)/h. However, the system power consumption varies little with time, staying at around 27 kW. In this way, the working conditions of the heat storage tank are mainly divided into two stages: 1:00-10:00 is the heat release mode, and 10:00-20:00 is the thermal storage mode. The heat storage capacity first changes from approximately 250 kW to 0 in the 1:00-10:00 stage, and then from 0 to approximately 460 kW in the 10:00-20:00 stage. In addition, the heat supply of the system is maintained at a relatively stable level at all times by switching the heat storage and heat release modes of the heat storage tank. In this case, the maximum system COP is 6.4420. The operating cost and the geothermal energy utilization coefficient are 300.02 and 0.8524, respectively. 3. Taking the geothermal energy utilization coefficient as the objective function, it can be seen that the optimization effects are similar to the effect of taking the system COP as the objective function. At this time, the maximum geothermal energy utilization coefficient is 0.8527. The operating cost and the system COP are 300.74 and 6.4413, respectively. 4. Comparing the three objective functions, it can be revealed that the operating costs are opposite to the optimization effects of the system COP, while the values of the geothermal energy utilization coefficient are not much different under different objective functions. Conceptualization, C.Z. and B.L. ; methodology, J.Z. and B.L. ; software, C.Q. ; validation, J.Z., B.L. and Y.W. ; formal analysis, J.Z. and C.Q. ; investigation, B.L. and J.Z. ; resources, B.L. ; data curation, B.L. and Y.W.