The invention discloses a control method and
system for energy efficiency optimization of a workshop
air conditioning system of a constant-temperature and constant-
humidity factory. The control method comprises the steps that a load prediction model is trained, and the constant-temperature and constant-
humidity factory is partitioned to set importance weights for all air conditioners; based on a
reinforcement learning algorithm, multi-objective optimization is carried out on an
air conditioning system composed of all air conditioners, the
reinforcement learning algorithm takes adjustment of operation parameters of all the air conditioners as actions, and the actions of each iteration and environment parameters, detected in real time, of a constant-temperature and constant-
humidity factory workshop are input into a load prediction model; the predicted cold load, the predicted
heat load and the predicted dehumidification amount of each air conditioner are obtained, and the performance coefficient of each air conditioner is calculated; and weighting the performance coefficient of each air conditioner according to the
importance weight, and constructing a reward function for multi-objective optimization by combining
a weighting result and the environment parameters, detected in real time, of the constant-temperature and constant-humidity factory workshop. According to the invention, the optimal optimization of the constant-temperature and constant-humidity factory among the lowest
energy consumption, the most stable control and the highest precision is realized.