Day-ahead electricity price forecasting using artificial intelligence-based algorithms

Basit Öğe Kaydı
dc.contributor.authorYorat, Emre
dc.contributor.authorOzbek, Necdet Sinan
dc.contributor.authorZor, Kasim
dc.contributor.authorSaribulut, Lutfu
dc.date.accessioned2025-01-06T17:29:44Z
dc.date.available2025-01-06T17:29:44Z
dc.date.issued2023
dc.description2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023 -- 20 November 2023 through 21 November 2023 -- Virtual, Online -- 196787
dc.description.abstractDeregulation and privatization of electricity markets has brought greater attention to electricity price forecasting (EPF) problem in day-ahead and intraday markets since a reliable forecast ensures market participants develop bidding strategies that aim to maximize their profit. Nonlinear and non-stationary characteristics of electricity prices ensemble a barrier in front of an accurate forecast and have required researchers to analyze the effects of exogenous variables such as economic metrics and neighboring countries' prices. In this paper, three different artificial intelligence-based algorithms namely multiple linear regression (MLR), autoregressive integrated moving average (ARIMA) with exogenous variables, and extreme gradient boosting decision trees (XGBoost) are applied to forecast day-ahead electricity prices of the Turkish electricity market by considering the aforementioned exogenous variables. Test results have shown that the XGBoost model has superior results in the error metrics than the other employed methods. Substantial error decrease in symmetric mean absolute percentage error, normalized root mean square error, normalized mean absolute error, and mean absolute scaled error metrics by 19.256%, 19.834%, 23.060%, and 23.016% is observed with respect to the closest performing MLR method on the test set. © 2023 IEEE.
dc.identifier.doi10.1109/3ICT60104.2023.10391547
dc.identifier.endpage126
dc.identifier.isbn979-835030777-1
dc.identifier.scopus2-s2.0-85184665318
dc.identifier.startpage121
dc.identifier.urihttps://doi.org/10.1109/3ICT60104.2023.10391547
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1326
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectartificial intelligence (AI)
dc.subjectautoregressive integrated moving average (ARIMA)
dc.subjectday-ahead electricity price forecasting (EPF)
dc.subjectextreme gradient boosting decision trees (XGBoost)
dc.subjectmultiple linear regression (MLR)
dc.titleDay-ahead electricity price forecasting using artificial intelligence-based algorithms
dc.typeConference Object

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu