HVAC Optimal Control with the Multistep-Actor Critic Algorithm in Large Action Spaces

Abstract EN

We propose an optimization method, named as the Multistep-Actor Critic (MAC) algorithm, which uses the value-network and the action-network, where the action-network is based on the deep Q-network (DQN).

The proposed method is intended to solve the problem of energy conservation optimization of heating, ventilating, and air-conditioning (HVAC) system in a large action space, principally for the cases with high computation and convergence time.

The method employs the multistep action-network and search tree to generate the original state and then selects the optimal state based on the value-network for the original and the adjacent states.

The results from the application of the MAC algorithm to a simulation problem on the TRNSYS system, where the simulation problem is referring to a real supertall building in Hong Kong, have shown that the proposed MAC algorithm balances control actions between different HVAC subsystems.

Further, it substantially saves the computational time while maintaining a good energy conservation performance.