How Generative AI Understands the Balance of Energy, Efficiency, and Human Experience

摘要

Balancing energy efficiency and occupant comfort in building HVAC systems is a critical challenge. While generative AI shows promise, its application has been hindered by a reliance on simulations and the inherent instability of its numerical predictions. This paper presents ''Office-in-the-Loop,'' a cyber-physical system leveraging generative AI in a real-world office. Our real-world experiments resolve the energy-comfort trade-off, achieving up to 47.92% energy savings with a 26.36% comfort improvement. We introduce a novel prompting technique, ''Data-Driven Reasoning,'' which compels the AI to justify its predictions with data. This simple addition improves prediction accuracy within ±0.5°C from 50% to 92.31%, paving the way for reliable, AI-driven building automation.