Pandemic-Aware Day-Ahead Demand Forecasting using Ensemble Learning

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorArjomandi-Nezhad, Ali
dc.contributor.authorAhmadi, Amirhossein
dc.contributor.authorTaheri, Saman
dc.contributor.authorFotuhi-Firuzabad, Mahmud
dc.contributor.authorMoeini-Aghtaie, Moein
dc.contributor.authorLehtonen, Matti
dc.contributor.departmentSharif University of Technology
dc.contributor.departmentUniversity of Calgary
dc.contributor.departmentIndiana University-Purdue University Indianapolis
dc.contributor.departmentPower Systems and High Voltage Engineering
dc.contributor.departmentDepartment of Electrical Engineering and Automation
dc.date.accessioned2022-02-02T07:50:48Z
dc.date.available2022-02-02T07:50:48Z
dc.date.issued2022-01
dc.descriptionPublisher Copyright: Author
dc.description.abstractElectricity demand forecast is necessary for power systems' operation scheduling and management. However, power consumption is uncertain and depends on several factors. Moreover, since the onset of covid-19, the electricity consumption pattern went through significant changes across the globe, which made the forecasting demand more challenging. This is mainly due to the fact that pandemic-driven restrictions changed people's lifestyles and work activities. This calls for new forecasting algorithms to more effectively handle these conditions. In this paper, ensemble-based machine learning models are utilized for this task. The lockdown temporal policies are added to the feature set in order to make the model capable of correcting itself in pandemic situations and enhance data quality for the forecasting task. Several ensemble-based machine learning models are examined for the short-term country-level demand prediction model. Besides, the quantile random forest regression is implemented for a probabilistic point of view. For case studies, the models are trained for predicting Germany's country-level demand. The results indicate that ensemble models, especially boosting and bagging-boosting models, are capable of accurate country-level demand forecast. Besides, the majority of these models are robust against missing the pandemic policy data. However, utilizing the pandemic policy data as features increases the forecasting accuracy during the pandemic situation significantly. Furthermore, the probabilistic quantile regression demonstrated high accuracy for the aforementioned case study.en
dc.description.versionPeer revieweden
dc.format.extent9
dc.format.mimetypeapplication/pdf
dc.identifier.citationArjomandi-Nezhad , A , Ahmadi , A , Taheri , S , Fotuhi-Firuzabad , M , Moeini-Aghtaie , M & Lehtonen , M 2022 , ' Pandemic-Aware Day-Ahead Demand Forecasting using Ensemble Learning ' , IEEE Access , vol. 10 , pp. 7098-7106 . https://doi.org/10.1109/ACCESS.2022.3142351en
dc.identifier.doi10.1109/ACCESS.2022.3142351
dc.identifier.issn2169-3536
dc.identifier.otherPURE UUID: 55a2ad54-c9db-4675-b080-1d25d7a11c4f
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/55a2ad54-c9db-4675-b080-1d25d7a11c4f
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85123282907&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/78861583/ELEC_Arjomandi_Nezhad_etal_Pandemic_Aware_Day_Ahead_Demand_Forecasting_IEEE_Access_2022.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/112754
dc.identifier.urnURN:NBN:fi:aalto-202202021651
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofseriesIEEE Accessen
dc.relation.ispartofseriesVolume 10en
dc.rightsopenAccessen
dc.subject.keywordCOVID-19
dc.subject.keywordCOVID-19 Pandemic
dc.subject.keywordDecision Tree Ensembles
dc.subject.keywordDecision trees
dc.subject.keywordDemand forecasting
dc.subject.keywordDemand Forecasting
dc.subject.keywordLoad modeling
dc.subject.keywordMachine learning
dc.subject.keywordPandemics
dc.subject.keywordPredictive models
dc.subject.keywordProbabilistic
dc.subject.keywordUncertainty
dc.titlePandemic-Aware Day-Ahead Demand Forecasting using Ensemble Learningen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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