The present application relates to the technical field of digital
temperature control, in particular to an
HVAC energy-saving
adaptive control system and method based on AI optimization, which comprises an
internal zone heat load analysis module, an external
heat penetration quantification module, a wind-water linkage cold quantity optimization module, a cold-
electricity conversion efficiency measurement module and an
actuator driving reconstruction module. In the present application, by analyzing the superposition process of
indoor air specific
enthalpy and
human body heat dissipation, the disturbance value caused by
building envelope thermal inertia is calculated, the energy efficiency attenuation amplitude under variable frequency working condition is quantified, the execution mechanism control loop parameters are reconstructed by using
reinforcement learning, the
heat load prediction accuracy is effectively improved, the matching degree of cold quantity supply and actual demand is strengthened, the equipment
operating frequency is optimized to suppress efficiency decline, the real-time monitoring and dynamic compensation of
refrigeration system energy efficiency ratio are realized,
mechanical wear and tear and hydraulic imbalance fluctuation are greatly reduced,
air conditioning operation stability is enhanced, and the
system can achieve efficient energy-saving adjustment goal under all working conditions.