Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorFang, Haizhouen_US
dc.contributor.authorTan, Hongweien_US
dc.contributor.authorDai, Ningfangen_US
dc.contributor.authorLiu, Zhaohuien_US
dc.contributor.authorKosonen, Ristoen_US
dc.contributor.departmentDepartment of Mechanical Engineeringen
dc.contributor.groupauthorEnergy Conversion and Systemsen
dc.contributor.organizationTongji Universityen_US
dc.date.accessioned2023-05-31T10:52:09Z
dc.date.available2023-05-31T10:52:09Z
dc.date.issued2023-05en_US
dc.descriptionFunding Information: This study was supported by the China Scholarship Council (CSC). Funding Information: This research was supported by the National Key R&D Program of China (Grant no. 2017YFC0704200). Publisher Copyright: © 2023 by the authors.
dc.description.abstractFor the management of building operations, hourly building energy consumption prediction (HBECP) is critical. Many factors, such as energy types, expected day intervals, and acquired feature types, significantly impact HBECP. However, the existing training sample selection methods, especially during transitional seasons, are unable to properly adapt to changes in operational conditions. The key feature search selection (KFSS) approach is proposed in this study. This technique ensures a quick response to changes in the parameters of the predicted day while enhancing the model’s accuracy, stability, and generalization. The best training sample set is found dynamically based on the similarity between the feature on the projected day and the historical data, and feature scenario analysis is used to make the most of the acquired data features. The hourly actual data in two years are applied to a major office building in Zhuhai, China as a case study. The findings reveal that, as compared to the original methods, the KFSS method can track daily load well and considerably enhance prediction accuracy. The suggested training sample selection approach can enhance the accuracy of prediction days by 14.5% in spring and 4.9% in autumn, according to the results. The proposed feature search and feature extraction strategy are valuable for enhancing the robustness of data-driven models for HBECP.en
dc.description.versionPeer revieweden
dc.format.extent23
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationFang, H, Tan, H, Dai, N, Liu, Z & Kosonen, R 2023, ' Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search ', Sustainability (Switzerland), vol. 15, no. 9, 7458 . https://doi.org/10.3390/su15097458en
dc.identifier.doi10.3390/su15097458en_US
dc.identifier.issn2071-1050
dc.identifier.otherPURE UUID: cd064159-9d24-4f4c-876e-8706211c7bd1en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/cd064159-9d24-4f4c-876e-8706211c7bd1en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85159307879&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/111852792/sustainability_15_07458_v2.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/121174
dc.identifier.urnURN:NBN:fi:aalto-202305313509
dc.language.isoenen
dc.publisherMDPI AG
dc.relation.ispartofseriesSustainability (Switzerland)en
dc.relation.ispartofseriesVolume 15, issue 9en
dc.rightsopenAccessen
dc.subject.keywordfeature searchen_US
dc.subject.keywordhourly predictionen_US
dc.subject.keywordmachine learningen_US
dc.subject.keywordscenario analysisen_US
dc.subject.keywordtraining set selectionen_US
dc.titleHourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Searchen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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