Bridging Machine Learning and Physics for Scalable Long-Term Building Temperature Prediction (Student Abstract)

Rohan Saha; Tushar Shinde

AAAI 2026technical0 citations

Bridging Machine Learning and Physics for Scalable Long-Term Building Temperature Prediction (Student Abstract)

Abstract

Building temperature prediction is crucial for energy optimization and control in smart cities. We present a physics-enhanced XGBoost framework in a multi-stage sequential scaling approach. Starting from single-zone, single-day predictions, we progressively scale to multi-zone, multi-year forecasts using real-world data from Google

BibTeX

@inproceedings{aaai2026_bridgingmachinel,
  title = {Bridging Machine Learning and Physics for Scalable Long-Term Building Temperature Prediction (Student Abstract)},
  author = {Rohan Saha and Tushar Shinde},
  booktitle = {AAAI 2026},
  year = {2026}
}