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Application of deep Q-networks for model-free optimal control balancing between different HVAC systems

机译：深度Q网络在不同HVAC系统之间无模型最佳控制平衡的应用

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摘要

A deep Q-network (DQN) was applied for model-free optimal control balancing between different HVAC systems. The DQN was coupled to a reference office building: an EnergyPlus simulation model provided by the U.S. Department of Energy. The building was air-conditioned with four air-handling units (AHUs), two electric chillers, a cooling tower, and two pumps. EnergyPlus simulation results for eleven days (July 1-11) and three subsequent days (July 12-14) were used to improve the DQN policy and test the optimal control. The optimization goal was to minimize the building's energy use while maintaining the indoor CO2 concentration below 1,000 ppm. It was revealed that the DQN-a reinforcement learning method-can improve its control policy based on prior actions, states, and rewards. The DQN lowered the total energy usage by 15.7% in comparison with the baseline operation while maintaining the indoor CO2 concentration below 1,000 ppm. Compared to model predictive control, the DQN does not require a simulation model, or a predetermined prediction horizon, thus delivering model-free optimal control. Furthermore, it was demonstrated that the DQN can find balanced control actions between different energy consumers in the building, such as chillers, pumps, and AHUs.
机译：应用了深度Q-Network（DQN）用于不同HVAC系统之间的无模型最佳控制平衡。 DQN耦合到参考办公楼：美国能源部提供的能量分布模型。该建筑用四个空气处理单位（Ahus），两个电气冷却器，冷却塔和两个泵有空调。 EnergyPlus仿真结果11天（7月1日至11日）和三天（7月12日至14日）用于改善DQN政策并测试最佳控制。优化目标是最小化建筑的能量使用，同时将室内二氧化碳浓度保持在1,000ppm以下。据透露，DQN-A强化学习方法 - 可以根据先前的行动，州和奖励来改善其控制策略。与基线操作相比，DQN将总能量使用降低了15.7％，同时将室内CO2浓度保持在1,000ppm以下。与模型预测控制相比，DQN不需要模拟模型或预定的预测地平线，从而提供无模型的最佳控制。此外，据证明DQN可以在建筑物中的不同能量消费者之间找到平衡的控制动作，例如冷却器，泵和Ahus。

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