A deep learning framework for heat demand forecasting using time–frequency representations of decomposed features
- DOI
- 10.1016/j.egyai.2026.100704
- Published
- 2026-05
- Container
- Energy and AI
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.egyai.2026.100704,
title = {A deep learning framework for heat demand forecasting using time–frequency representations of decomposed features},
author = {Adithya Ramachandran and Satyaki Chatterjee and Thorkil Flensmark B. Neergaard and Maximilian Oberndoerfer and Andreas Maier and Siming Bayer},
year = {2026},
journal = {Energy and AI},
doi = {10.1016/j.egyai.2026.100704},
url = {https://doi.org/10.1016/j.egyai.2026.100704}
}RIS
TY - JOUR TI - A deep learning framework for heat demand forecasting using time–frequency representations of decomposed features AU - Adithya Ramachandran AU - Satyaki Chatterjee AU - Thorkil Flensmark B. Neergaard AU - Maximilian Oberndoerfer AU - Andreas Maier AU - Siming Bayer PY - 2026 JO - Energy and AI DO - 10.1016/j.egyai.2026.100704 UR - https://doi.org/10.1016/j.egyai.2026.100704 ER -
APA
Ramachandran, A., Chatterjee, S., Neergaard, T. F. B., Oberndoerfer, M., Maier, A., & Bayer, S. (2026). A deep learning framework for heat demand forecasting using time–frequency representations of decomposed features. Energy and AI. https://doi.org/10.1016/j.egyai.2026.100704
Source records
- crossref · retrieved 2026-09-26T16:26:44.147Z