🤖 AI Summary
This study addresses the challenge of achieving efficient, safe, and adaptive control of water-cooled chillers and air-side systems in tropical commercial buildings. The authors propose the CQD-ERL controller, which introduces, for the first time in HVAC control, a context-aware quality-diversity (QD) evolutionary mechanism. By integrating evolutionary algorithms with the soft actor-critic (SAC) policy gradient method, the approach maintains a policy archive indexed jointly by operational context and behavioral descriptors, enabling the co-optimization of diverse, high-performing control policies. This framework supports dynamic selection of specialized policies tailored to prevailing weather conditions and load profiles. A deterministic safety shielding mechanism is incorporated to enforce critical constraints such as humidity levels and cooling tower approach temperature. Evaluated through full-year backtesting on a commercial building in Singapore, the proposed controller significantly outperforms the ASHRAE Guideline 36 baseline, achieving energy-efficient operation while rigorously maintaining safety constraints.
📝 Abstract
This paper proposes a contextual quality-diversity evolutionary reinforcement-learning controller, CQD-ERL, for the supervisory control of a tropical, water-cooled chiller plant and its associated air side. Rather than converging to a single scalarised policy, the controller maintains a product archive of specialised policies indexed jointly by a data- driven operating context, a cluster of daily weather and load regime, and a context-invariant behaviour descriptor, filled by a gradient-free evolutionary operator and a soft-actor-critic policy-gradient operator that share one replay buffer. Every action is filtered through a deterministic safety shield before execution. The controller is trained on a two-tier reduced-order environment representing the latent load, cooling-tower approach and humidity constraints of a Singapore commercial building, and is evaluated over a full annual backtest against an ASHRAE Guideline 36 baseline.