Research on multi-objective optimization configuration of solar ground source heat pump system using data-driven approach

Under the premise of ensuring the stable operation of solar coupled ground source heat pump systems (SGSHPs), optimizing the configuration to improve the thermal performance of the system and reduce the cost of the system area key measures to promote the popularization of such systems in cold regions. The SGSHPs models of three different operation modes are established in this paper, taking office buildings in the Shenyang area as an example and basing on the TRNSYS platform. The ground temperature imbalance rate and the total energy consumption of the system after thirty years of operation in different modes are compared and analyzed. The parallel transition season thermal storage mode with better energy saving potential is selected as the main research object, and six main design parameters are determined through sensitivity analysis, which are the number of buried pipe boreholes, solar collector area, thermal storage start temperature difference, thermal storage stop temperature difference, collector start temperature difference, and collector stop temperature difference. The 1000 groups of systems with different parameter combinations are simulated, and 1000 sample data are generated as a database.The multiple outputs support vector regression (MSVR) network was trained and validated using the sample database, and the particle swarm optimization (PSO) algorithm was used to optimize the hyper-parameters in the MSVR network, and the trained PSO-MSVR network could correlate the design variable parameters and performance parameters of SGSHPs. Finally, a multi-objective optimization model of the SGSHPs was established using a genetic algorithm, and the Pareto solution set for the optimization of the system configuration was obtained with the objectives of thermodynamic performance and economy of the system, and the optimal solution was obtained by using technique for order preference by similarity to ideal solution (TOPSIS) decision-making. Compared with the pre-optimization design of the heat balance method, the annual cost (AC) is reduced by 17.733 %, and the system COP (COP_sys) and winter solar direct supply ratio (f_solar) are improved by 29.174 % and 163.78 %, respectively. The data-driven optimization method proposed in this paper has a significant advantage in obtaining the optimized configuration parameters of the SGSHPs system with multi-objective optimization.