Short-term Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Management

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorEseye, Abinet Tesfayeen_US
dc.contributor.authorLehtonen, Mattien_US
dc.contributor.authorTukia, Tonien_US
dc.contributor.authorUimonen, Semenen_US
dc.contributor.authorMillar, R. Johnen_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen_US
dc.contributor.departmentPower Systems and High Voltage Engineeringen_US
dc.date.accessioned2020-02-12T10:46:18Z
dc.date.available2020-02-12T10:46:18Z
dc.date.issued2019en_US
dc.description.abstractThe electricity consumption profile of buildings are different from the typical load curves that represent the electricity consumption of large systems at the national or regional level. The electricity demand in buildings is many times lower than the region- or nation-wide demands. It is also much more volatile and stochastic, meaning that the conventional tools are not effective enough for straightforward application at a building demand level. In this paper, an integrated approach consisting of Hilbert-Huang Transform (HHT), Regrouping Particle Swarm Optimization (RegPSO) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is devised for 24h-ahead prediction of electric power consumption in buildings. The forecasts are used as input information for smart decisions of distributed energy management systems that control the optimal bidding and scheduling of energy resources for building energy communities. The effectiveness of the proposed forecasting approach is demonstrated using actual electricity demand data from various buildings in the Otaniemi area of Espoo, Finland. The prediction performance of the proposed approach for various building types (energy customer clusters), has been examined and statistical comparisons are presented. The prediction results are presented for future days with a one-hour time interval. The validation results demonstrate that the approach is able to forecast the buildings’ electricity demand with smaller error, outperforming five other approaches, and in reasonably short computation times.en
dc.description.versionPeer revieweden
dc.format.extent8
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationEseye , A T , Lehtonen , M , Tukia , T , Uimonen , S & Millar , R J 2019 , Short-term Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Management . in Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019 : Industrial Applications of Artificial Intelligence . IEEE International Conference on Industrial Informatics , IEEE , pp. 1103-1110 , IEEE International Conference on Industrial Informatics , Helsinki and Espoo , Finland , 22/07/2019 . https://doi.org/10.1109/INDIN41052.2019.8972188en
dc.identifier.doi10.1109/INDIN41052.2019.8972188en_US
dc.identifier.isbn978-1-7281-2927-3
dc.identifier.issn1935-4576
dc.identifier.issn2378-363X
dc.identifier.otherPURE UUID: 01aaf9c4-99e0-49ae-a80e-8667879d3507en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/01aaf9c4-99e0-49ae-a80e-8667879d3507en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/39092239/ELEC_Eseye_etal_Short_term_Forecasting_INDIN_2019_authoracceptedmanuscript.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/43037
dc.identifier.urnURN:NBN:fi:aalto-202002122106
dc.language.isoenen
dc.publisherIEEE
dc.relation.ispartofIEEE International Conference on Industrial Informaticsen
dc.relation.ispartofseriesProceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019en
dc.relation.ispartofseriesIEEE International Conference on Industrial Informaticsen
dc.rightsopenAccessen
dc.subject.keywordAIen_US
dc.subject.keywordANFISen_US
dc.subject.keywordBuildingen_US
dc.subject.keywordElectricity demanden_US
dc.subject.keywordEnergy managementen_US
dc.subject.keywordFeature extractionen_US
dc.subject.keywordForecastingen_US
dc.subject.keywordHHTen_US
dc.subject.keywordMachine learningen_US
dc.subject.keywordParameter optimizationen_US
dc.subject.keywordRegPSOen_US
dc.titleShort-term Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Managementen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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