End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers
Researchers' end-to-end learning method cuts energy costs 10% by repurposing data center waste heat.
A research team from Xi'an Jiaotong University has developed a novel AI-driven framework to optimize the complex, multi-energy systems powering modern buildings and data centers. Published in a paper titled "End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers," their method tackles a critical gap: few systems coordinate these two massive energy consumers to exploit their synergy. The key innovation is using waste heat from power-hungry data centers to enhance overall building energy efficiency, creating a closed-loop system.
Unlike conventional "predict-then-optimize" approaches that separate forecasting from decision-making, the researchers propose an end-to-end learning model. This unified framework trains prediction models for uncertain variables—like energy demand and renewable supply—directly alongside the system's operational optimization. The AI is guided to improve actual operational performance, not just prediction accuracy, which mitigates the impact of forecast errors. This holistic approach is a significant shift from standard practice.
Case studies using real-world data demonstrate the model's effectiveness. The end-to-end method improves the operational performance of the integrated energy system (IES) by about 7-9% compared to existing methods. Furthermore, the coordinated operation of buildings and data centers, specifically through waste heat recovery, leads to a substantial economic benefit, reducing the IES's total energy cost by approximately 10%. This research provides a scalable blueprint for making large-scale infrastructure more sustainable and cost-effective.
- Proposes a unified AI framework that coordinates energy use between buildings and data centers, exploiting waste heat recovery for a ~10% total cost reduction.
- Uses an end-to-end learning method that optimizes for system performance, not prediction accuracy, outperforming traditional "predict-then-optimize" models by 7-9%.
- Addresses a key challenge in operational optimization: accurately predicting multi-energy demand and supply under uncertainty from renewables.
Why It Matters
This AI model offers a practical path to significantly lower energy costs and carbon footprints for large commercial and tech infrastructure.