Abstract:Currently, reinforcement learning (RL) has shown great potential in energy saving in HVAC systems. However, in most cases, RL takes a relatively long period to explore the environment before obtaining an excellent control policy, which may lead to an increase in cost. To reduce the unnecessary waste caused by RL methods in exploration, we extended the deep forest-based deep Q-network (DF-DQN) from the prediction problem to the control problem, optimizing the running frequency of the cooling water pump and cool… Show more
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