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Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network

机译：利用深增强学习和经常性神经网络的空气处理单元最优控制

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摘要

Optimal control of heating, ventilation and air conditioning systems (HVACs) aims to minimize the energy consumption of equipment while maintaining the thermal comfort of occupants. Traditional rule-based control methods are not optimized for HVAC systems with continuous sensor readings and actuator controls. Recent developments in deep reinforcement learning (DRL) enabled control of HVACs with continuous sensor inputs and actions, while eliminating the need of building complex thermodynamic models. DRL control includes an environment, which approximates real-world HVAC operations; and an agent, that aims to achieve optimal control over the HVAC. Existing DRL control frameworks use simulation tools (e.g., EnergyPlus) to build DRL training environments with HVAC systems information, but oversimplify building geometrics. This study proposes a framework aiming to achieve optimal control over Air Handling Units (AHUs) by implementing longshort-term-memory (LSTM) networks to approximate real-world HVAC operations to build DRL training environments. The framework also implements state-of-the-art DRL algorithms (e.g., deep deterministic policy gradient) for optimal control over the AHUs. Three AHUs, each with two-years of building automation system (BAS) data, were used as testbeds for evaluation. Our LSTM-based DRL training environments, built using the first year's BAS data, achieved an average mean square error of 0.0015 across 16 normalized AHU parameters. When deployed in the testing environments, which were built using the second year's BAS data of the same AHUs, the DRL agents achieved 27%-30% energy saving comparing to the actual energy consumption, while maintaining the predicted percentage of discomfort (PPD) at 10%.
机译：加热，通风和空调系统（HVACS）的最佳控制旨在最大限度地减少设备的能耗，同时保持乘员的热舒适性。具有连续传感器读数和执行器控制的HVAC系统未优化基于规则的控制方法。最近的深度增强学习（DRL）的发展使HVAC控制了连续传感器输入和动作，同时消除了建筑复杂热力学模型的需要。 DRL控件包括一个环境，其近似现实世界HVAC操作;和一个代理人，旨在实现对HVAC的最佳控制。现有的DRL控制框架使用仿真工具（例如，EnergyPlus）来构建带有HVAC系统信息的DRL培训环境，而是过度简化的构建地理学测信息。本研究提出了一种框架，其旨在通过实现延长术语 - 存储器（LSTM）网络来实现对空气处理单元（AHUS）来实现最佳控制，以近似实现RACLL训练环境的真实HVAC操作。该框架还实现了最先进的DRL算法（例如，深度确定性政策梯度），以实现对AHU的最佳控制。三个Ahus，每个拥有两年的楼宇自动化系统（BAS）数据，被用作测试平台进行评估。我们基于LSTM的DRL培训环境，使用第一年的BAS数据建造，实现了16.0015的平均平均方误差，横跨16个归一化AHU参数。部署在使用同一AHU的第二年BAS数据建造的测试环境中，DRL代理商达到了27％-30％的节能与实际能耗相比，同时保持了预测的不适（PPD）的百分比10％。

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