The invention relates to the technical field of
refrigeration house energy-saving control, and provides a
refrigeration house energy-saving control method based on deep
reinforcement learning, which comprises the following steps: dividing a
refrigeration house according to a multi-scale grid, collecting data in real time, weighting and aggregating according to spatial characteristics, calculating spatial semantic codes and confidence indexes of each grid, and obtaining a standardized and traceable
state vector; real-time
energy consumption, grid temperature space gradient, long-term
exposure economic loss, event condition triggering and weighted summation form a reward function, and a deep strategy network is adopted to
train parameters in an accumulated reward mode; constructing a
grey box and residual
dynamic prediction model, shrinking the original temperature and
humidity constraint, solving the prediction model through
scenario under the shrinkage constraint, and carrying out weighted fusion on the depth strategy obtained by training and the solution of the prediction model according to confidence; and calculating a statistical value in the sliding window, constructing confidence distribution of strategy return, triggering a hierarchical response according to a detection result, and inputting all triggers into an audit log for threshold self-calibration.