A Unified Python Framework for Direct PPO-based Control of AHUs with Economizer Logic and CO2-Constrained Ventilation
arXiv:2605.24406v1 Announce Type: new 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 cu
The increasing availability of advanced DRL algorithms and computational power makes practical, real-time optimization of complex systems like HVAC more feasible.
This development can significantly reduce energy consumption in buildings, impacting overall energy grids and operational costs, while improving environmental quality.
Traditional rule-based HVAC controls can be superseded by more adaptive and efficient AI-driven systems, leading to smarter, more responsive building infrastructure.
- · Building owners and operators
- · Energy utilities
- · AI/ML developers and researchers
- · Manufacturers of smart building technologies
- · Legacy HVAC control system manufacturers (slow to adapt)
- · Inefficient energy providers
- · Buildings with outdated infrastructure
Widespread adoption could lead to substantial reductions in electricity demand from the building sector.
This efficiency gain could alleviate pressure on energy grids, potentially enabling more compute infrastructure deployment without overwhelming existing supply.
Reduced energy demand could slow the need for new power generation capacity, freeing up capital for other infrastructure investments or lowering operating costs for AI-heavy industries.
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