Forecasting electricity load by a novel recurrent extreme learning machines approach

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dc.authorid0000-0003-0710-0867en_US
dc.contributor.authorErtuğrul, Ömer Faruk
dc.date.accessioned2019-07-04T13:16:42Z
dc.date.available2019-07-04T13:16:42Z
dc.date.issued2016-06en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractGrowth in electricity demand also gives a rise to the necessity of cheaper and safer electric supply and forecasting electricity load plays a key role in this goal. In this study recurrent extreme learning machine (RELM) was proposed as a novel approach to forecast electricity load more accurately. In RELM, extreme learning machine (ELM), which is a training method for single hidden layer feed forward neural network, was adapted to train a single hidden layer Jordan recurrent neural network. Electricity Load Diagrams 2011-2014 dataset was employed to evaluate and validate the proposed approach. Obtained results were compared with traditional ELM, linear regression, generalized regression neural network and some other popular machine learning methods. Achieved root mean square errors (RMSE) by RELM were nearly twice less than obtained results by other employed machine learning methods. The results showed that the recurrent type ANNs had extraordinary success in forecasting dynamic systems and also time-ordered datasets with comparison to feed forward ANNs. Also, used time in the training stage is similar to ELM and they are extremely fast than the others. This study showed that the proposed approach can be applied to forecast electricity load and RELM has high potential to be utilized in modeling dynamic systems effectively.en_US
dc.identifier.citationErtuğrul, Ö F. (2016). Forecasting electricity load by a novel recurrent extreme learning machines approach. International Journal of Electrical Power & Energy Systems, 78, pp. 429-435. https://doi.org/10.1016/j.ijepes.2015.12.006en_US
dc.identifier.endpage435en_US
dc.identifier.issn0142-0615
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage429en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijepes.2015.12.006
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2195
dc.identifier.volume78en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.ijepes.2015.12.006en_US
dc.relation.journalInternational Journal of Electrical Power and Energy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectContext Neuronen_US
dc.subjectElectricity Load Forecastingen_US
dc.subjectRecurrent Extreme Learning Machineen_US
dc.subjectRecurrent Neural Networken_US
dc.titleForecasting electricity load by a novel recurrent extreme learning machines approachen_US
dc.typeArticleen_US

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