BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912367030304768 |
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| author | Lin, Xiachong Prabowo, Arian Razzak, Imran Xue, Hao Amos, Matthew Behrens, Sam Salim, Flora D. |
| author_facet | Lin, Xiachong Prabowo, Arian Razzak, Imran Xue, Hao Amos, Matthew Behrens, Sam Salim, Flora D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14175 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics Lin, Xiachong Prabowo, Arian Razzak, Imran Xue, Hao Amos, Matthew Behrens, Sam Salim, Flora D. Computational Engineering, Finance, and Science Computers and Society Human-Computer Interaction 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. |
| title | BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics |
| topic | Computational Engineering, Finance, and Science Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2412.14175 |