Abstract
Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called Long-Short-Term Memory (LSTM) networks. Optimization of thermal comfort alongside energy consumption is the goal in tuning this RL controller. The test platform, our office space, is designed using SketchUp. Using OpenStudio, the HVAC system is installed in the office. The control schemes (ideal thermal comfort, a traditional control and the RL control) are implemented in MATLAB. Using the Building Control Virtual Test Bed (BCVTB), the control of the thermostat schedule during each sample time is implemented for the office in EnergyPlus alongside local weather data. Results from training and validation indicate that the RL controller improves thermal comfort by an average of 15% and energy efficiency by an average of 2.5% as compared to other strategies mentioned.
Highlights
According to the U.S Energy Information Administration, the average energy consumed in the buildings sector for residential and commercial users accounts for 20.1% of global energy consumption worldwide
The optimization variable module implemented in MATLAB 2016 uses the Predicted Mean Vote (PMV) [32], which is the thermal comfort measure, and the energy consumed during the sample time to calculate cost
It is observed that during both training and validation, the Reinforcement Learning (RL) controller was able to maintain the average PMV within an admissible range while saving energy compared to the Bideal case
Summary
According to the U.S Energy Information Administration, the average energy consumed in the buildings sector for residential and commercial users accounts for 20.1% of global energy consumption worldwide. Processes 2017, 5, 46 to the high non-linearity in building dynamics coupled with uncertainties such as weather, energy pricing, etc., these PID controllers require extensive re-tuning or auto-tuning capabilities [13], which increases the difficulty and complexity of the control problem. Due to these challenging aspects of HVAC control, various advanced control methods have been investigated, ranging from gain-scheduling [14], non-linear/robust model predictive control [15,16,17,18]. A thermostat schedule is computed using an RL controller
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
