Research Article

A COMPREHENSIVE STUDY ON FORECASTING TURKEY'S ENERGY DEMAND WITH THE MARINE PREDATORS ALGORITHM

Volume: 27 Number: 2 June 3, 2024
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A COMPREHENSIVE STUDY ON FORECASTING TURKEY'S ENERGY DEMAND WITH THE MARINE PREDATORS ALGORITHM

Abstract

The energy demand is increasing day by day, and it is of great importance to predict this demand. This study was conducted to estimate Turkey's energy demand between 1979 - 2015 with the recently proposed marine predators algorithm (MPA). To determine the weights of the linear and quadratic regression models used in the study is utilized from the MPA. According to the studies, MPA is used for this purpose for the first time in the literature. The results obtained by the MPA for sum-squared-error and total-relative-percentage-error metrics were compared with algorithms well-known in the literature differential evolution, Archimedes optimization, moth flame optimization, and grey wolf optimizer. Unlike other studies in the literature, performance comparisons are not only based on the best value; it was made according to the best, worst, average, and standard deviation values. The results showed that MPA has a more successful and stable structure than the compared algorithms in the energy demand forecasting problem.

Keywords

Supporting Institution

There is no institution that receives support.

Ethical Statement

There is no conflict of interest with any institution or person.

Thanks

Thanks in advance for your efforts.

References

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Details

Primary Language

Turkish

Subjects

Reinforcement Learning , Evolutionary Computation , Modelling and Simulation

Journal Section

Research Article

Publication Date

June 3, 2024

Submission Date

January 3, 2024

Acceptance Date

February 14, 2024

Published in Issue

Year 1970 Volume: 27 Number: 2

APA
Özkış, A. (2024). DENİZ YIRTICILARI ALGORİTMASI İLE TÜRKİYE’NİN ENERJİ TALEBİNİN TAHMİN EDİLMESİNE YÖNELİK KAPSAMLI BİR ÇALIŞMA. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 615-630. https://doi.org/10.17780/ksujes.1413432

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