Publication
Forecasting Heating and Cooling Energy Demand in an Office Building using Machine Learning Methods
| datacite.subject.sdg | 07:Energias Renováveis e Acessíveis | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| dc.contributor.author | Godinho, Xavier | |
| dc.contributor.author | Bernardo, Hermano | |
| dc.contributor.author | Oliveira, Filipe Tadeu | |
| dc.contributor.author | Sousa, João | |
| dc.date.accessioned | 2025-09-04T14:10:00Z | |
| dc.date.available | 2025-09-04T14:10:00Z | |
| dc.date.issued | 2020-07 | |
| dc.description | EISBN - 978-1-7281-5678-1 | |
| dc.description | Godinho, Xavier - Scopus ID: 57219308624 | |
| dc.description.abstract | Forecasting heating and cooling energy demand in buildings plays a critical role in supporting building management and operation. Thus, analysing the energy consumption pattern of a building could help in the design of potential energy savings and also in operation fault detection, while contributing to provide proper indoor environmental conditions to the building's occupants.This paper aims at presenting the main results of a study consisting in forecasting the hourly heating and cooling demand of an office building located in Lisbon, Portugal, using machine learning models and analysing the influence of exogenous variables on those predictions. In order to forecast the heating and cooling demand of the considered building, some traditional models, such as linear and polynomial regression, were considered, as well as artificial neural networks and support vector regression, oriented to machine learning. The input parameters considered in the development of those models were the hourly heating and cooling energy historical records, the occupancy, solar gains through glazing and the outside dry-bulb temperature.The models developed were validated using the mean absolute error (MAE) and the root mean squared error (RMSE), used to compare the values obtained from machine learning models with data obtained through a building energy simulation performed on an adequately calibrated model.The proposed exploratory analysis is integrated in a research project focused on applying machine learning methodologies to support energy forecasting in buildings. Hence, the research line proposed in this article corresponds to a preliminary project task associated with feature selection/extraction and evaluation of potential use of machine learning methods. | eng |
| dc.description.sponsorship | This work was partially supported by the European Regional Development Fund in the framework of COMPETE 2020 Programme through projects UIDB/00308/2020, ESGRIDS (POCI-01-0145-FEDER-016434) and MAnAGER (POCI-01-0145-FEDER-028040), and the FCT - Portuguese Foundation for Science and Technology. | |
| dc.identifier.citation | X. Godinho, H. Bernardo, F. T. Oliveira and J. C. Sousa, "Forecasting Heating and Cooling Energy Demand in an Office Building using Machine Learning Methods," 2020 International Young Engineers Forum (YEF-ECE), Costa da Caparica, Portugal, 2020, pp. 1-6, doi: https://doi.org/10.1109/YEF-ECE49388.2020.9171807. | |
| dc.identifier.doi | 10.1109/yef-ece49388.2020.9171807 | |
| dc.identifier.isbn | 978-1-7281-5679-8 | |
| dc.identifier.isbn | 978-1-7281-5678-1 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/13979 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | IEEE Canada | |
| dc.relation | Institute for Systems Engineering and Computers at Coimbra - INESC Coimbra | |
| dc.relation.hasversion | https://ieeexplore.ieee.org/document/9171807 | |
| dc.relation.ispartof | 2020 International Young Engineers Forum (YEF-ECE) | |
| dc.rights.uri | N/A | |
| dc.subject | Energy Forecasting in Buildings | |
| dc.subject | Heating and Cooling Demand | |
| dc.subject | Machine Learning | |
| dc.title | Forecasting Heating and Cooling Energy Demand in an Office Building using Machine Learning Methods | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Institute for Systems Engineering and Computers at Coimbra - INESC Coimbra | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00308%2F2020/PT | |
| oaire.citation.conferenceDate | 2020-07-03 | |
| oaire.citation.conferencePlace | Costa da Caparica, Portugal | |
| oaire.citation.endPage | 6 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | Proceedings - 2020 International Young Engineers Forum, YEF-ECE 2020 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Bernardo | |
| person.familyName | Oliveira | |
| person.familyName | Sousa | |
| person.givenName | Hermano | |
| person.givenName | Filipe Tadeu | |
| person.givenName | João | |
| person.identifier.orcid | 0000-0002-5290-6424 | |
| person.identifier.orcid | 0000-0002-0410-4291 | |
| person.identifier.orcid | 0000-0002-7567-4910 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| relation.isAuthorOfPublication | c58ec34e-5fbf-41cc-ae25-716ad0abb964 | |
| relation.isAuthorOfPublication | f3c004bf-5759-4b8c-87c8-350bd598d035 | |
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| relation.isProjectOfPublication | 254d9223-2e3b-4754-bae9-c98986d80921 | |
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- Forecasting heating and cooling energy demand in buildings plays a critical role in supporting building management and operation. Thus, analysing the energy consumption pattern of a building could help in the design of potential energy savings and also in operation fault detection, while contributing to provide proper indoor environmental conditions to the building's occupants.This paper aims at presenting the main results of a study consisting in forecasting the hourly heating and cooling demand of an office building located in Lisbon, Portugal, using machine learning models and analysing the influence of exogenous variables on those predictions. In order to forecast the heating and cooling demand of the considered building, some traditional models, such as linear and polynomial regression, were considered, as well as artificial neural networks and support vector regression, oriented to machine learning. The input parameters considered in the development of those models were the hourly heating and cooling energy historical records, the occupancy, solar gains through glazing and the outside dry-bulb temperature.The models developed were validated using the mean absolute error (MAE) and the root mean squared error (RMSE), used to compare the values obtained from machine learning models with data obtained through a building energy simulation performed on an adequately calibrated model.The proposed exploratory analysis is integrated in a research project focused on applying machine learning methodologies to support energy forecasting in buildings. Hence, the research line proposed in this article corresponds to a preliminary project task associated with feature selection/extraction and evaluation of potential use of machine learning methods.
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