Heat demand forecasting in District Heating Network using XGBoost algorithm
E3S Web Conf., 323 (2021) 00004
Published online: 2021-11-10
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
Machine learning for district heating and cooling: A review of open problems and deployment barriers
Bilal Rinchi, Sameer Al-Dahidi, Mohammad Alrbai and Osama AyadiEnergy and Buildings 368 117878 (2026)
https://doi.org/10.1016/j.enbuild.2026.117878
784 (2025)
https://doi.org/10.1109/RAAICON69033.2025.11502497
533 (2025)
https://doi.org/10.1109/WIECON-ECE69386.2025.11526270
Data-Driven Model for Heat Load Prediction in Buildings Connected to District Heating Networks
Alaeddine Hajri, Roberto Garay-Marinez, Ana M. Macarulla and Mohamed Amin Ben SassiEnergy 136684 (2025)
https://doi.org/10.1016/j.energy.2025.136684
Exploring Heat Demand Forecasting in District Heating Networks Using Random Parameter Linear Regression Model
Hesham Ali, Andrei Dedov and Anna VolkovaEnvironmental and Climate Technologies 28 (1) 670 (2024)
https://doi.org/10.2478/rtuect-2024-0052
A Privacy Preserving Multi-Center Federated Learning framework for District Heating Forecast
Kais Dai, Esteban Fabello González and Rebeca Isabel García-BetancesEnergy and Buildings 115164 (2024)
https://doi.org/10.1016/j.enbuild.2024.115164
Forecasting of Daily Heat Production in a District Heating Plant Using a Neural Network
Adam Maryniak, Marian Banaś, Piotr Michalak and Jakub SzymiczekEnergies 17 (17) 4369 (2024)
https://doi.org/10.3390/en17174369