[Autodesk Inc., https://www.autodesk.com/products/revit, Retrieved Dec 2024.]Search in Google Scholar
[Chen K., Hu J., Yu L., Zheng M., Sun S., He D., Lin J., A Data-driven Model of Pipe Diameter and Insulation Thickness Optimization for District Heating Systems, Journal of Physics: Conference Series, Volume 2166, International Conference on Frontiers of Electrical Power & Energy Systems, 2022.]Search in Google Scholar
[DesignBuilder Software Ltd, https://designbuilder.co.uk, Retrieved Nov 2024.]Search in Google Scholar
[European Commission, 2050 Long-Term Strategy, (https://climate.ec.europa.eu/eu-action/climate-strategies-targets/2050-long-term-strategy_en), Retrieved Nov 2024.]Search in Google Scholar
[Fumo N., A review on the basics of building energy estimation, Renewable and Sustainable Energy Reviews, Volume 31, 2014, Pages 53-60.]Search in Google Scholar
[Geysen D, De Somer O., Johansson C., Brage J., Vanhoudt D., Operational thermal load forecasting in district heating networks using machine learning and expert advice, Energy and Buildings, Volume 162, 2018, Pages 144-153.]Search in Google Scholar
[IEA DHC, Annex XIII Project 03, 2023 (https://www.iea-dhc.org/the-research/annexes/annex-xiii/annex-xiii-project-03)]Search in Google Scholar
[Kaggle, dataset: Fault Detection and Diagnosis in District Heating, (https://www.kaggle.com/datasets/mathieuvallee/ai-dhc/data)]Search in Google Scholar
[Liu Y., Chen H., Zhang L., Feng Z., Enhancing building energy efficiency using a random forest model: A hybrid prediction approach, Energy Reports, Volume 7, 2021, Pages 5003-5012.]Search in Google Scholar
[Mbiydzenyuy G., Nowaczyk S., Knuttson H., Vanhoudt D., Brage J., Calikus E., Opportunities for Machine Learning in District Heating, Applied Sciences. 2021; 11(13):6112.]Search in Google Scholar
[Ntakolia C., Anagnostis A., Moustakidis S. et al., Machine learning applied on the district heating and cooling sector: a review, Energy Systems, 2022; Volume 13, Pages 1-30.]Search in Google Scholar
[Palasz P., Przysowa R., Using Different ML Algorithms and Hyperparameter Optimization to Predict Heat Meters’ Failures, Applied Sciences. 2019; 9(18):3719.]Search in Google Scholar
[Sakkas N.P., Abang R., Thermal load prediction of communal district heating systems by applying data-driven machine learning methods, Energy Reports, Volume 8, 2022, Pages 1883-1895.]Search in Google Scholar
[Song J., Zhang L., Xue G., Ma Y., Gao S., Jiang Q., Predicting hourly heating load in a district heating system based on a hybrid CNN-LSTM model, Energy and Buildings, Volume 243, 2021, 110998.]Search in Google Scholar
[United Nations, World Urbanization Prospects, 2018, (https://www.un.org/development/desa/pd/news/world-urbanization-prospects-2018)]Search in Google Scholar
[United Nations, World Population Prospects, 2024, (https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd_2024_wpp_2024_advance_unedited_0.pdf).]Search in Google Scholar
[Vallee M., Wissocq T., Gaoua Y., Lamaison N., Generation and evaluation of a synthetic dataset to improve fault detection in district heating and cooling systems, Energy, Volume 283, 2023, 128387.]Search in Google Scholar
[Wang P., Poovendran P., Manokaran K.B., Fault detection and control in integrated energy system using machine learning, Sustainable Energy Technologies and Assessments, Volume 47, 101366.]Search in Google Scholar
[Werner S., International review of district heating and cooling, Energy, Volume 137, 2017, Pages 617-631.]Search in Google Scholar
