For ground source heat pump (GSHP) systems, conventional control strategies often suffer from significant hysteresis, leading to energy waste and occupant discomfort. This study proposes and validates a novel Prediction Self-Adaptive Control (PSAC) technology that hybridizes deep learning foresight with robust engineering feedback loops. The architecture integrates a CNN-LSTM model to forecast building thermal loads with high fidelity, and this prediction drives a macro-scale unit commitment module that optimizes chiller sequencing. Simultaneously, a micro-scale self-adaptive feedback mechanism dynamically resets the chilled water supply temperature and modulates pump frequency to eliminate the residual error between the predicted state and the actual building demand, ensuring precise load matching. Field implementation in a 62,500 m2 residential complex in Shanghai demonstrated that the CNN-LSTM model achieved a load forecasting accuracy within a ±10% error margin, the PSAC strategy significantly outperformed baseline constant-temperature controls, maintaining indoor temperatures between 23 and 26 °C and relative humidity between 30 and 55% and the system achieved a weekly average System Coefficient of Performance (SCOP) of 3.91 compared to the baseline of 3.30, resulting in an 15.6% reduction in total energy consumption. By decoupling predictive planning from adaptive execution, the system offers a scalable, robust, and highly efficient solution for the decarbonization of HVAC systems in complex climate zones.
Cui et al. (Mon,) studied this question.