Using TimeGPT forecasts to run building HVAC systems cheaper and greener.

What is it about?

Controlling large building HVAC systems is challenging because the environment is constantly changing (non-stationary), which confuses conventional AI. Traditional fixed-rule systems react too slowly, leading to energy waste. Our research solves this by integrating TimeGPT, a powerful, pre-trained AI foundation model, to generate highly accurate zero-shot forecasts of critical factors like weather and occupancy instantly. By feeding these future forecasts directly into our Deep Reinforcement Learning (DRL) controller, the AI learns to anticipate changes, not just react. This predictive integration accelerates DRL convergence and stabilizes control, resulting in significant energy savings and reduced temperature fluctuations.

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Why is it important?

Modern buildings are responsible for a significant portion of global energy consumption. Improving HVAC efficiency is therefore a huge opportunity for decarbonization and cost reduction. Current AI-based HVAC control methods struggle with real-world complexity, often requiring extensive, time-consuming training for every new building or environment. Our work is important because it introduces a highly scalable and computationally efficient solution to this non-stationarity problem. By using TimeGPT's "zero-shot" forecasting ability, we eliminate the need for time-consuming, task-specific training for the forecasting module. This radically simplifies the deployment of advanced DRL controllers in new or diverse buildings. The proposed pipeline makes AI control more robust and practical for commercial use, enabling building managers to implement cutting-edge, energy-saving DRL policies with greater stability and faster setup, leading to more comfortable environments and substantial energy savings globally.

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