Fallahnejad M. et al. District heating potential in the EU-27: Evaluating the impacts of heat demand reduction and market share growth. Applied Energy 2024:353:122154. https://doi.org/10.1016/j.apenergy.2023.122154
Krumins A., Lebedeva K., Tamane A., Millers R. Possibilities of Balancing Buildings Energy Demand for Increasing Energy Efficiency in Latvia. Environmental and Climate Technologies 2022:26(1):98–114. https://doi.org/10.2478/rtuect-2022-0009
Pakere I., Feofilovs M., Lepiksaar K., Vītoliņš V., Blumberga D. Multi-source district heating system full decarbonization strategies: Technical, economic, and environmental assessment. Energy 2023:285:129296. https://doi.org/10.1016/j.energy.2023.129296
Rieksta M., Zarins E., Bazbauers G. Potential Role of Green Hydrogen in Decarbonization of District Heating Systems: A Review. Environmental and Climate Technologies 2023:27(1):545–558. https://doi.org/10.2478/rtuect-2023-0040
Fritz M., Savin M., Aydemir A. Usage of excess heat for district heating – Analysis of enabling factors and barriers. J Clean Prod 2022:363:132370. https://doi.org/10.1016/j.jclepro.2022.132370
Madurai Elavarasan R., Pugazhendhi R., Irfan M., Mihet-Popa L., Khan I. A., Campana P. E. State-of-the-art sustainable approaches for deeper decarbonization in Europe – An endowment to climate neutral vision. Renewable and Sustainable Energy Reviews 2022:159:112204. https://doi.org/10.1016/j.rser.2022.112204
Volkova A., Koduvere H., Pieper H. Large-scale heat pumps for district heating systems in the Baltics: Potential and impact. Renewable and Sustainable Energy Reviews 2022:167:112749. https://doi.org/10.1016/j.rser.2022.112749
Pieper H. et al. Optimal usage of low temperature heat sources to supply district heating by heat pumps. Proceedings of ECOS 2017: 30th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, 2017.
Pardo-Bosch F., Blanco A., Mendoza N., Libreros B., Tejedor B., Pujadas P. Sustainable deployment of energy efficient district heating: city business model. Energy Policy 2023:181:113701. https://doi.org/10.1016/j.enpol.2023.113701
Rashidi Zadeh D., Derakhshan G., Mehdi Hakimi S., Abdi B. An Economic and Environmental Optimization Model in a Micro Grid with Demand Response. Environmental and Climate Technologies 2022:26(1):730–741. https://doi.org/10.2478/rtuect-2022-0056
Bujalski M., Madejski P., Fuzowski K. Heat demand forecasting in District Heating Network using XGBoost algorithm. In E3S Web of Conferences, EDP Sciences. 2021. https://doi.org/10.1051/e3sconf/202132300004
Zhang J., Hu Y., Yuan Y., Yuan H., Mei N. Accuracy improvement of the load forecasting in the district heating system by the informer-based framework with the optimal step size selection. Energy 2024:291:130347. https://doi.org/10.1016/j.energy.2024.130347
Johansson C., Bergkvist M., Geysen D., De Somer O., Lavesson N., Vanhoudt D. Operational Demand Forecasting in District Heating Systems Using Ensembles of Online Machine Learning Algorithms. Energy Procedia 2017:116:208–216. https://doi.org/10.1016/j.egypro.2017.05.068
Apostolopoulou A., Jimenez-Bescos C., Cavazzi S., Boyd D. Impact of Climate Change on the Heating Demand of Buildings. A District Level Approach. Environmental and Climate Technologies 2023:27(1):900–911. https://doi.org/10.2478/rtuect-2023-0066
Rušeljuk P. et al. Factors Affecting the Improvement of District Heating. Case Studies of Estonia and Serbia. Environmental and Climate Technologies 2021:24(3):521–533. https://doi.org/10.2478/rtuect-2020-0121
Latõšov E., Volkova A., Hlebnikov A., Siirde A. Technical improvement potential of large district heating network: Application to the Case of Tallinn, Estonia. Energy Procedia 2018:149:337–344. https://doi.org/10.1016/j.egypro.2018.08.197
Kemper N., Heider M., Pietruschka D., Hähner J. Forecasting of residential unit’s heat demands: a comparison of machine learning techniques in a real-world case study. Energy Systems 2023. https://doi.org/10.1007/s12667-023-00579-y
Etxebarria M., Oregi X., Grijalba O., Hernández R. J. Relationship between energy demand, indoor thermal behaviour and temperature-related health risk concerning passive energy refurbishment interventions. Environmental and Climate Technologies 2020:24(2):348–363. https://doi.org/10.2478/rtuect-2020-0078
Motuzienė V., Bielskus J., Lapinskienė V., Rynkun G. Office building’s occupancy prediction using extreme learning machine model with different optimization algorithms,” Environmental and Climate Technologies 2021:25(1):525–536. https://doi.org/10.2478/rtuect-2021-0038
Potočnik P., Škerl P., Govekar E. Machine-learning-based multi-step heat demand forecasting in a district heating system. Energy Build 2021:233:110673. https://doi.org/10.1016/j.enbuild.2020.110673
Eseye A. T., Lehtonen M. Short-Term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models. IEEE Trans Industr Inform 2020:16(12):7743–7755. https://doi.org/10.1109/TII.2020.2970165
Moradzadeh A., Mohammadi-Ivatloo B., Abapour M., Anvari-Moghaddam A., Roy S. S. Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis. Institute of Electrical and Electronics Engineers Inc. 2022:10. https://doi.org/10.1109/ACCESS.2021.3136091
Kontopoulou V. I., Panagopoulos A. D., Kakkos I., Matsopoulos G. K. A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks. Future Internet 2023:15(8):255. https://doi.org/10.3390/fi15080255
Ma T., Antoniou C., Toledo T. Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transp Res Part C: Emerg Technol 2020:111:352–372. https://doi.org/10.1016/j.trc.2019.12.022
Shakeel A., Chong D., Wang J. Load forecasting of district heating system based on improved FB-Prophet model. Energy 2023:278:127637. https://doi.org/10.1016/j.energy.2023.127637
Zdravković M., Ćirić I., Ignjatović M. Explainable heat demand forecasting for the novel control strategies of district heating systems. Annu Rev Control 2022:53:405–413. https://doi.org/10.1016/j.arcontrol.2022.03.009
Golla A., Geis J., Loy T., Staudt P., Weinhardt C. An operational strategy for district heating networks: application of data-driven heat load forecasts. Energy Informatics 2020:3(1):22. https://doi.org/10.1186/s42162-020-00125-5
Ntakolia C., Anagnostis A., Moustakidis S., Karcanias N. Machine learning applied on the district heating and cooling sector: a review. Energy systems 2022:13:1–30. https://doi.org/10.1007/s12667-020-00405-9
Chaganti R. et al. Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model. Sensors 2022:22(19):7692. https://doi.org/10.3390/s22197692
Bünning F., Heer P., Smith R. S., Lygeros J. Improved day ahead heating demand forecasting by online correction methods. Energy Build 2020:211:109821. https://doi.org/10.1016/j.enbuild.2020.109821
Mannering F. L., Shankar V., Bhat C. R. Unobserved heterogeneity and the statistical analysis of highway accident data. Anal Methods Accid Res 2016:11:1–16. https://doi.org/10.1016/j.amar.2016.04.001
Hamed M. M., Al-Eideh B. M. An exploratory analysis of traffic accidents and vehicle ownership decisions using a random parameters logit model with heterogeneity in means. Anal Methods Accid Res 2020:25:100116. https://doi.org/10.1016/j.amar.2020.100116
Ghiasi A., Fountas G., Anastasopoulos P., Mannering F. Statistical assessment of peer opinions in higher education rankings: The case of US engineering graduate programs. Journal of Applied Research in Higher Education 2019:11(3):481–492. https://doi.org/10.1108/JARHE-09-2018-0196
Provencher B., Moore R. A discussion of ‘using angler characteristics and attitudinal data to identify environmental preference classes: A latent-class model. Environ Resour Econ (Dordr) 2006:34(1):117–124. https://doi.org/10.1007/s10640-005-3793-8
Hamed M. M., Ali H., Abdelal Q. Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances. Energy 2022:255:124510. https://doi.org/10.1016/j.energy.2022.124510
Verwiebe P. A., Seim S., Burges S., Schulz L., Müller-Kirchenbauer J. Modeling energy demand—a systematic literature review. Energies 2021:14(23):7859. https://doi.org/10.3390/en14237859
Klyuev R. V. et al. Methods of Forecasting Electric Energy Consumption: A Literature Review. Energies 2022:15(23):8919. https://doi.org/10.3390/en15238919
Rušeljuk P., Lepiksaar K., Siirde A., Volkova A. Economic dispatch of chp units through district heating network’s demand-side management. Energies (Basel) 2021:14(15):4553. https://doi.org/10.3390/en14154553
Neagu O., Teodoru M. C. The relationship between economic complexity, energy consumption structure and greenhouse gas emission: Heterogeneous panel evidence from the EU countries. Sustainability (Switzerland) 2019:11(2):497. https://doi.org/10.3390/su11020497
Huang Y., Zhao Y., Wang Z., Liu X., Liu H., Fu Y. Explainable district heat load forecasting with active deep learning. Applied Energy 2023:350:121753. https://doi.org/10.1016/j.apenergy.2023.121753