Incorporating AI technologies into digital infrastructure offers transformative potential for energy management, particularly in enhancing energy efficiency and supporting net-zero objectives. However, the complexity of IoT-generated datasets often poses a significant challenge, hindering the translation of research insights into practical, real-world applications. This paper presents the design of an interactive visualization tool, BiTSA. The tool enables building managers to interpret complex energy data quickly and take immediate, data-driven actions based on real-time insights. By integrating advanced forecasting models with an intuitive visual interface, our solution facilitates proactive decision-making, optimizes energy consumption, and promotes sustainable building management practices. BiTSA will empower building managers to optimize energy consumption, control demand-side energy usage, and achieve sustainability goals.
@article{arxiv.2412.14175,
title = {BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics},
author = {Xiachong Lin and Arian Prabowo and Imran Razzak and Hao Xue and Matthew Amos and Sam Behrens and Flora D. Salim},
journal= {arXiv preprint arXiv:2412.14175},
year = {2025}
}