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Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Eseye, Abinet Tesfaye
dc.contributor.author Lehtonen, Matti
dc.contributor.author Tukia, Toni
dc.contributor.author Uimonen, Semen
dc.contributor.author Millar, R. John
dc.date.accessioned 2019-09-03T13:43:22Z
dc.date.available 2019-09-03T13:43:22Z
dc.date.issued 2019-01-01
dc.identifier.citation Eseye , A T , Lehtonen , M , Tukia , T , Uimonen , S & Millar , R J 2019 , ' Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems ' , IEEE Access , vol. 7 , 8744520 , pp. 91463-91475 . https://doi.org/10.1109/ACCESS.2019.2924685 en
dc.identifier.issn 2169-3536
dc.identifier.other PURE UUID: 202e4f31-d0f0-4578-8356-80ad29957ddd
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/202e4f31-d0f0-4578-8356-80ad29957ddd
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85069790288&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/36432926/ELEC_Eseye_etal_Maschine_Learning_Based_IEEEAccess_2019_publishedversion.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/40021
dc.description.abstract Improved performance electricity demand forecast can provide decentralized energy system operators, aggregators, managers, and other stakeholders with essential information for energy resource scheduling, demand response management, and energy market participation. Most previous methodologies have focused on predicting the aggregate amount of electricity demand at national or regional scale and disregarded the electricity demand for small-scale decentralized energy systems (buildings, energy communities, microgrids, local energy internets, etc.), which are emerging in the smart grid context. Furthermore, few research groups have performed attribute selection before training predictive models. This paper proposes a machine learning (ML)-based integrated feature selection approach to obtain the most relevant and nonredundant predictors for accurate short-term electricity demand forecasting in distributed energy systems. In the proposed approach, one of the ML tools-binary genetic algorithm (BGA) is applied for the feature selection process and Gaussian process regression (GPR) is used for measuring the fitness score of the features. In order to validate the effectiveness of the proposed approach, it is applied to various building energy systems located in the Otaniemi area of Espoo, Finland. The findings are compared with those achieved by other feature selection techniques. The proposed approach enhances the quality and efficiency of the predictor selection, with minimal chosen predictors to achieve improved prediction accuracy. It outperforms the other evaluated feature selection methods. Besides, a feedforward artificial neural network (FFANN) model is implemented to evaluate the forecast performance of the selected predictor subset. The model is trained using two-year hourly dataset and tested with another one-year hourly dataset. The obtained results verify that the FFANN forecast model based on the BGA-GPR FS selected training feature subset has achieved an annual MAPE of 1.96%, which is a very acceptable andpromising value for electricity demand forecasting in small-scale decentralized energy systems. en
dc.format.extent 13
dc.format.extent 91463-91475
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries IEEE Access en
dc.relation.ispartofseries Volume 7 en
dc.rights openAccess en
dc.title Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Power Systems and High Voltage Engineering
dc.contributor.department Department of Electrical Engineering and Automation
dc.subject.keyword Binary genetic algorithm
dc.subject.keyword Decentralized energy system
dc.subject.keyword Electricity demand forecasting
dc.subject.keyword Feature selection
dc.subject.keyword Feedforward artificial neural network
dc.subject.keyword Fitness evaluation measure
dc.subject.keyword Gaussian process regression
dc.subject.keyword Machine learning
dc.subject.keyword Smart grid
dc.identifier.urn URN:NBN:fi:aalto-201909035063
dc.identifier.doi 10.1109/ACCESS.2019.2924685
dc.type.version publishedVersion

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