🤖 AI Summary
This study addresses the challenge of simultaneously optimizing energy efficiency and thermal comfort in building HVAC systems under nonlinear dynamics and stochastic loads, which conventional control strategies struggle to manage effectively. The authors propose an end-to-end deep reinforcement learning framework that directly controls the air handling unit (AHU) using the Proximal Policy Optimization (PPO) algorithm within a custom Python environment. The approach integrates a second-order RC thermal model with a CO₂ dynamic mass balance model to jointly optimize temperature and ventilation. A novel hierarchical airflow logic ensures indoor CO₂ concentrations remain at or below 1000 ppm, while an enthalpy-based economizer logic enables free cooling. Compared to traditional on–off control and a PID benchmark tuned via genetic algorithms, the proposed method demonstrates superior performance in both temperature stability and overall energy efficiency, validating its potential for intelligent building energy management.
📝 Abstract
Optimizing HVAC (Heating, Ventilation and Air Conditioning) can enhance a building's energy efficiency while providing comfort levels for its occupants. Using conventional control systems to maintain HVAC functions is often difficult because of the nonlinear characteristics of a building envelope as it experiences stochastic load variations over time. This paper presents a new approach to optimizing HVAC systems through the use of Deep Reinforcement Learning (DRL) algorithms and the Proximal Policy Optimization (PPO) algorithm implemented in a custom Python performance environment. The DRL system uses a second order resistor-capacitor thermal model and an integrated dynamic mass balance of CO2 to replicate the complex physics associated with buildings. One major innovation of this study is a "Hierarchical Flow Logic," which provides the means to ensure that indoor air quality (IAQ) is maintained by overriding the accepted actions of the agent that cause CO2 to exceed 1000 ppm. In addition, an enthalpy-based economiser is used to create free cooling from the outdoor environment. The experimental data shows that compared to PID controllers tuned by GA or traditional On-Off controls, a PPO agent has better temperature stability and energy efficiency overall. An end-to-end pipeline provides an avenue for robust and generalized solutions to help implement smart building energy management within the context of real hardware implementation.