A Data-Driven Model To Forecast Multi-Step Ahead Time Series of Turkish Daily Electricity Load

dc.authorid Ünlü, Kamil Demirberk/0000-0002-2393-6691
dc.authorscopusid 57210105250
dc.authorwosid Ünlü, Kamil Demirberk/AAL-5952-2020
dc.contributor.author Unlu, Kamil Demirberk
dc.contributor.author Ünlü, Kamil Demirberk
dc.contributor.author Ünlü, Kamil Demirberk
dc.contributor.other Industrial Engineering
dc.contributor.other Industrial Engineering
dc.date.accessioned 2024-07-05T15:17:40Z
dc.date.available 2024-07-05T15:17:40Z
dc.date.issued 2022
dc.department Atılım University en_US
dc.department-temp [Unlu, Kamil Demirberk] Atilim Univ, Dept Math, TR-06830 Ankara, Turkey en_US
dc.description Ünlü, Kamil Demirberk/0000-0002-2393-6691 en_US
dc.description.abstract It is critical to maintain a balance between the supply and the demand for electricity because of its non-storable feature. For power-producing facilities and traders, an electrical load is a piece of fundamental and vital information to have, particularly in terms of production planning, daily operations, and unit obligations, among other things. This study offers a deep learning methodology to model and forecast multistep daily Turkish electricity loads using the data between 5 January 2015, and 26 December 2021. One major reason for the growing popularity of deep learning is the creation of new and creative deep neural network topologies and significant computational advancements. Long Short-Term Memory (LSTM), Gated Recurrent Network, and Convolutional Neural Network are trained and compared to forecast 1 day to 7 days ahead of daily electricity load. Three different performance metrics including coefficient of determination (R-2), root mean squared error, and mean absolute error were used to evaluate the performance of the proposed algorithms. The forecasting results on the test set showed that the best performance is achieved by LSTM. The algorithm has an R-2 of 0.94 for 1 day ahead forecast, and the metric decreases to 0.73 in 7 days ahead forecast. en_US
dc.identifier.citationcount 13
dc.identifier.doi 10.3390/electronics11101524
dc.identifier.issn 2079-9292
dc.identifier.issue 10 en_US
dc.identifier.scopus 2-s2.0-85129716270
dc.identifier.uri https://doi.org/10.3390/electronics11101524
dc.identifier.uri https://hdl.handle.net/20.500.14411/1765
dc.identifier.volume 11 en_US
dc.identifier.wos WOS:000801763700001
dc.identifier.wosquality Q2
dc.institutionauthor Ünlü, Kamil Demirberk
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 20
dc.subject neural network modeling en_US
dc.subject electricity load forecasting en_US
dc.subject deep learning en_US
dc.subject artificial neural networks en_US
dc.subject time series analysis en_US
dc.title A Data-Driven Model To Forecast Multi-Step Ahead Time Series of Turkish Daily Electricity Load en_US
dc.type Article en_US
dc.wos.citedbyCount 16
dspace.entity.type Publication
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