Short-term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorEseye, Abinet Tesfayeen_US
dc.contributor.authorLehtonen, Mattien_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.groupauthorPower Systems and High Voltage Engineeringen
dc.date.accessioned2020-09-25T07:06:50Z
dc.date.available2020-09-25T07:06:50Z
dc.date.issued2020en_US
dc.description.abstractThe increasing growth in the energy demand calls for robust actions to design and optimize energy-related assets for efficient and economic energy supply and demand within a smart grid setup. This article proposes a novel integrated machine learning (ML) technique to forecast the heat demand of buildings in a district heating system. The proposed short-term (24h-ahead) heat demand forecasting model is based on the integration of empirical mode decomposition (EMD), imperialistic competitive algorithm (ICA), and support vector machine (SVM). The proposed model also embeds an ML-based feature selection (FS) technique combining binary genetic algorithm and Gaussian process regression to obtain the most important and nonredundant variables that can constitute the input predictor subset to the forecasting model. The model is developed using a two-year (2015-2016) hourly dataset of actual district heat demand obtained from various buildings in the Otaniemi area of Espoo, Finland. Several variables from different domains such as seasonality (calendar), weather, occupancy, and heat demand are used to construct the initial feature space for FS process. Short-term forecasting models are also implemented using the Persistence approach as a reference and other eight ML approaches: artificial neural network (ANN), genetic algorithm combined with ANN (GA-ANN), ICA-ANN, SVM, GA-SVM, ICA-SVM, EMD-GA-ANN, and EMD-ICA-ANN. The performance of the proposed EMD-ICA-SVM-based forecasting model is tested using an out-of-sample one-year (2017) hourly dataset of district heat consumption of various building types. Comparative analysis of the forecasting performance of the models was performed. The obtained results demonstrate that the devised model forecasts the heat demand with improved performance evaluated using various accuracy metrics. Moreover, the devised model achieves outperformed forecasting accuracy enhancement, compared to the other nine evaluated models.en
dc.description.versionPeer revieweden
dc.format.extent11
dc.format.extent7743-7755
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationEseye, A T & Lehtonen, M 2020, ' Short-term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models ', IEEE Transactions on Industrial Informatics, vol. 16, no. 12, 8990012, pp. 7743-7755 . https://doi.org/10.1109/TII.2020.2970165en
dc.identifier.doi10.1109/TII.2020.2970165en_US
dc.identifier.issn1941-0050
dc.identifier.otherPURE UUID: ee757694-d2de-4acb-a160-0827205bcdd2en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/ee757694-d2de-4acb-a160-0827205bcdd2en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85092105332&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/51621124/ELEC_Tesfaye_Eseye_Lehtonen_Short_Term_Forecasting_IEEETraIndInf_16_2020_finalpublishedversion.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/46631
dc.identifier.urnURN:NBN:fi:aalto-202009255561
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofseriesIEEE Transactions on Industrial Informaticsen
dc.relation.ispartofseriesVolume 16, issue 12en
dc.rightsopenAccessen
dc.subject.keywordBuildingen_US
dc.subject.keywordData-driven modelen_US
dc.subject.keywordDistrict heatingen_US
dc.subject.keywordEnergy managementen_US
dc.subject.keywordMachine learningen_US
dc.subject.keywordSmart griden_US
dc.titleShort-term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Modelsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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