{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:10:22Z","timestamp":1760058622433,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT (Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia)","award":["UIDB\/04152\/2020"],"award-info":[{"award-number":["UIDB\/04152\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Environments"],"abstract":"<jats:p>Space heating consumption prediction is critical for energy management and efficiency, directly impacting sustainability and efforts to reduce greenhouse gas emissions. Accurate models enable better demand forecasting, promote the use of green energy, and support decarbonization goals. However, existing models often lack precision due to limited feature sets, suboptimal algorithm choices, and limited access to weather data, which reduces generalizability. This study addresses these gaps by evaluating various Machine Learning and Deep Learning models, including K-Nearest Neighbors, Support Vector Regression, Decision Trees, Linear Regression, XGBoost, Random Forest, Gradient Boosting, AdaBoost, Long Short-Term Memory, and Gated Recurrent Units. We utilized space heating consumption data from the European Central Bank Headquarters office as a case study. We employed a methodology that involved splitting the features into three categories based on the correlation and evaluating model performance using Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and R-squared metrics. Results indicate that XGBoost consistently outperformed other models, particularly when utilizing all available features, achieving an R2 value of 0.966 using the weather data from the building weather station. This model\u2019s superior performance underscores the importance of comprehensive feature sets for accurate predictions. The significance of this study lies in its contribution to sustainable energy management practices. By improving the accuracy of space heating consumption forecasts, our approach supports the efficient use of green energy resources, aiding in the global efforts towards decarbonization and reducing carbon footprints in urban environments.<\/jats:p>","DOI":"10.3390\/environments12040131","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T20:38:26Z","timestamp":1745267906000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Sustainable Energy: Predictive Models for Space Heating Consumption at the European Central Bank"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3331-2373","authenticated-orcid":false,"given":"Fernando","family":"Almeida","sequence":"first","affiliation":[{"name":"NOVA Information Management School, Universidade NOVA de Lisboa, 1070-312 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8793-1451","authenticated-orcid":false,"given":"Mauro","family":"Castelli","sequence":"additional","affiliation":[{"name":"NOVA Information Management School, Universidade NOVA de Lisboa, 1070-312 Lisboa, Portugal"}]},{"given":"Nadine","family":"C\u00f4rte-Real","sequence":"additional","affiliation":[{"name":"NOVA Information Management School, Universidade NOVA de Lisboa, 1070-312 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"unstructured":"EU. Council (2010). Directive 2010\/31\/EU of the European Parliament and of the Council of 19 May 2010 on the Energy Performance of Buildings (Recast), European Commission.","key":"ref_1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s40807-023-00078-9","article-title":"A comparative study of machine learning and deep learning methods for energy balance prediction in a hybrid building-renewable energy system","volume":"10","author":"Mirjalili","year":"2023","journal-title":"Sustain. Energy Res."},{"doi-asserted-by":"crossref","unstructured":"Mar\u00edn-Garc\u00eda, D., Bienvenido, D.H., Nieto-Juli\u00e1n, E., Campos, J.J.M., Farinha, M.J.O., and Farinha, F. (2019). Analysis of the Regulations That Affect Energy Efficiency with Respect to Consumption of HVAC System for Residential Buildings in Southern Spain and Portugal, Springer.","key":"ref_3","DOI":"10.1007\/978-3-030-30938-1_38"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106116","DOI":"10.1016\/j.jobe.2023.106116","article-title":"Analysing energy poverty in warm climate zones in Spain through artificial intelligence","volume":"68","year":"2023","journal-title":"J. Build. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1016\/j.rser.2012.02.049","article-title":"A review on the prediction of building energy consumption","volume":"16","author":"Zhao","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"unstructured":"DOE (2024, May 22). DOE 2. Available online: https:\/\/www.doe2.com\/.","key":"ref_6"},{"unstructured":"(2024, May 22). E. Plus. Energy Plus. Available online: https:\/\/energyplus.net\/.","key":"ref_7"},{"unstructured":"TRNSYS (2024, May 22). Transient System Simulation Tool. Available online: https:\/\/www.trnsys.com\/.","key":"ref_8"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/S0360-1323(00)00026-3","article-title":"Computer-aided building energy analysis techniques","volume":"36","year":"2001","journal-title":"Build. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.enbuild.2004.09.007","article-title":"A method of formulating energy load profile for domestic buildings in the UK","volume":"37","author":"Yao","year":"2005","journal-title":"Energy Build."},{"doi-asserted-by":"crossref","unstructured":"Shi, C., Zheng, J., Wang, Y., Gan, C., Zhang, L., and Sheldon, B.W. (2025). Machine Learning-Driven Scattering Efficiency Prediction in Passive Daytime Radiative Cooling. Atmosphere, 16.","key":"ref_11","DOI":"10.3390\/atmos16010095"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"64","DOI":"10.35940\/ijeat.C4383.13030224","article-title":"Artificial Intelligence Applications in Natural Gas Industry: A Literature Review","volume":"13","author":"Nuthakki","year":"2024","journal-title":"Int. J. Eng. Adv. Technol."},{"doi-asserted-by":"crossref","unstructured":"Muhamad, W.N.W., Zain, M.Y.M., Wahab, N., Aziz, N.H.A., and Kadir, R.A. (2010, January 27\u201329). Energy Efficient Lighting System Design for Building. Proceedings of the ISMS 2010\u2014UKSim\/AMSS 2010 International Conference on Intelligent Systems, Modelling and Simulation, Liverpool, UK.","key":"ref_13","DOI":"10.1109\/ISMS.2010.59"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1109\/TCST.2021.3057630","article-title":"Stochastic Optimal Control of HVAC System for Energy-Efficient Buildings","volume":"30","author":"Yang","year":"2022","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.enbuild.2014.11.077","article-title":"Improving energy efficiency via smart building energy management systems: A comparison with policy measures","volume":"88","author":"Rocha","year":"2015","journal-title":"Energy Build."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e1487","DOI":"10.1002\/widm.1487","article-title":"Review of artificial intelligence-based question-answering systems in healthcare","volume":"13","author":"Budler","year":"2023","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"doi-asserted-by":"crossref","unstructured":"Hartmann, T., Moawad, A., Schockaert, C., Fouquet, F., and Le Traon, Y. (2019, January 15\u201320). Meta-Modelling Meta-Learning. Proceedings of the 2019 ACM\/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS), Munich, Germany.","key":"ref_17","DOI":"10.1109\/MODELS.2019.00014"},{"key":"ref_18","first-page":"100363","article-title":"Renewable energy management in smart grids by using big data analytics and machine learning","volume":"9","author":"Mostafa","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110601","DOI":"10.1016\/j.enbuild.2020.110601","article-title":"A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data","volume":"231","author":"Liu","year":"2021","journal-title":"Energy Build."},{"doi-asserted-by":"crossref","unstructured":"Almuhaini, S.H., and Sultana, N. (2023). Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management. Energies, 16.","key":"ref_20","DOI":"10.3390\/en16042035"},{"key":"ref_21","first-page":"1747","article-title":"Using bees algorithm and artificial neural network to forecast world carbon dioxide emission","volume":"33","author":"Behrang","year":"2011","journal-title":"Energy Sources Part A Recovery Util. Environ. Eff."},{"key":"ref_22","first-page":"149","article-title":"Conversational AI and LLM\u2019s Current and Future Impacts in Improving and Scaling Health Services","volume":"14","author":"Nuthakki","year":"2023","journal-title":"Int. J. Comput. Eng. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"133536","DOI":"10.1016\/j.energy.2024.133536","article-title":"A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism","volume":"312","author":"Cui","year":"2024","journal-title":"Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116085","DOI":"10.1016\/j.energy.2019.116085","article-title":"Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms","volume":"188","author":"Xue","year":"2019","journal-title":"Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"118676","DOI":"10.1016\/j.energy.2020.118676","article-title":"A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour","volume":"212","author":"Li","year":"2020","journal-title":"Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.enbuild.2015.02.052","article-title":"Ensemble of various neural networks for prediction of heating energy consumption","volume":"94","year":"2015","journal-title":"Energy Build."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.enbuild.2017.10.006","article-title":"Sample data selection method for improving the prediction accuracy of the heating energy consumption","volume":"158","author":"Yuan","year":"2018","journal-title":"Energy Build."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110673","DOI":"10.1016\/j.enbuild.2020.110673","article-title":"Machine-learning-based multi-step heat demand forecasting in a district heating system","volume":"233","author":"Govekar","year":"2021","journal-title":"Energy Build."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111647","DOI":"10.1016\/j.enbuild.2021.111647","article-title":"Prediction of heating energy consumption with operation pattern variables for non-residential buildings using LSTM networks","volume":"255","author":"Jang","year":"2022","journal-title":"Energy Build."},{"key":"ref_30","first-page":"3403150","article-title":"Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study","volume":"2016","author":"Dalipi","year":"2016","journal-title":"Appl. Comput. Intell. Soft Comput."},{"doi-asserted-by":"crossref","unstructured":"Moradzadeh, A., Mansour-Saatloo, A., Mohammadi-Ivatloo, B., and Anvari-Moghaddam, A. (2020). Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings. Appl. Sci., 10.","key":"ref_31","DOI":"10.3390\/app10113829"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"409","DOI":"10.5267\/j.dsl.2020.3.004","article-title":"A comprehensive comparative analysis of machine learning models for predicting heating and cooling loads","volume":"9","author":"Abdelkader","year":"2020","journal-title":"Decis. Sci. Lett."},{"doi-asserted-by":"crossref","unstructured":"Shen, Y., Wei, R., and Xu, L. (2018). Energy consumption prediction of a greenhouse and optimization of daily average temperature. Energies, 11.","key":"ref_33","DOI":"10.3390\/en11010065"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/ACCESS.2021.3136091","article-title":"Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis","volume":"10","author":"Moradzadeh","year":"2022","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s12273-020-0752-9","article-title":"Evaluating multiple parameters dependency of base temperature for heating degree-days in building energy prediction","volume":"14","author":"Meng","year":"2021","journal-title":"Build. Simul."},{"doi-asserted-by":"crossref","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., and Greer, K. (2003, January 3\u20137). KNN model-based approach in classification. Proceedings of the on the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy.","key":"ref_36","DOI":"10.1007\/978-3-540-39964-3_62"},{"doi-asserted-by":"crossref","unstructured":"Zhang, F., and O\u2019Donnell, L.J. (2020). Support Vector Regression, Academic Press.","key":"ref_37","DOI":"10.1016\/B978-0-12-815739-8.00007-9"},{"key":"ref_38","first-page":"275","article-title":"An introduction to decision tree modeling","volume":"18","author":"Myles","year":"2004","journal-title":"J. Chemom. A J. Chemom. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1002\/wics.1198","article-title":"Linear regression","volume":"4","author":"Su","year":"2012","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","key":"ref_40","DOI":"10.1145\/2939672.2939785"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Natekin, A., and Knoll, A. (2013). Gradient boosting machines, a tutorial. Front. Neurorobot., 7.","key":"ref_42","DOI":"10.3389\/fnbot.2013.00021"},{"unstructured":"Solomatine, D.P., and Shrestha, D.L. (2004, January 25\u201329). AdaBoost. RT: A boosting algorithm for regression problems. Proceedings of the IEEE International Joint Conference on Neural Networks, Budapest, Hungary.","key":"ref_43"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"unstructured":"Yao, K., Cohn, T., Vylomova, K., Duh, K., and Dyer, C. (2015). Depth-Gated Recurrent Neural Networks. arXiv.","key":"ref_45"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"83241","DOI":"10.1109\/ACCESS.2021.3087345","article-title":"Energy Production Forecasting from Solar Photovoltaic Plants Based on Meteorological Parameters for Qassim Region, Saudi Arabia","volume":"9","author":"Alaraj","year":"2021","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Dadhich, M., Pahwa, M.S., Jain, V., and Doshi, R. (2021). Predictive Models for Stock Market Index Using Stochastic Time Series ARIMA Modeling in Emerging Economy. Advances in Mechanical Engineering, Springer.","key":"ref_47","DOI":"10.1007\/978-981-16-0942-8_26"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s40808-017-0387-8","article-title":"Performance of NCUM global weather modeling system in predicting the extreme rainfall events over the central India during the Indian summer monsoon 2016","volume":"3","author":"Shrivastava","year":"2017","journal-title":"Model. Earth Syst. Environ."},{"doi-asserted-by":"crossref","unstructured":"Brahimi, T. (2019). Using artificial intelligence to predict wind speed for energy application in Saudi Arabia. Energies, 12.","key":"ref_49","DOI":"10.3390\/en12244669"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"129866","DOI":"10.1016\/j.energy.2023.129866","article-title":"District heating load patterns and short-term forecasting for buildings and city level","volume":"289","author":"Hua","year":"2024","journal-title":"Energy"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"124283","DOI":"10.1016\/j.energy.2022.124283","article-title":"District heating load prediction algorithm based on bidirectional long short-term memory network model","volume":"254","author":"Cui","year":"2022","journal-title":"Energy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"123350","DOI":"10.1016\/j.energy.2022.123350","article-title":"District heater load forecasting based on machine learning and parallel CNN-LSTM attention","volume":"246","author":"Chung","year":"2022","journal-title":"Energy"}],"container-title":["Environments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3298\/12\/4\/131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:18:38Z","timestamp":1760030318000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3298\/12\/4\/131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,21]]},"references-count":52,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["environments12040131"],"URL":"https:\/\/doi.org\/10.3390\/environments12040131","relation":{},"ISSN":["2076-3298"],"issn-type":[{"type":"electronic","value":"2076-3298"}],"subject":[],"published":{"date-parts":[[2025,4,21]]}}}