LSTM, XGBOOST AND RANDOM FOREST MODELS IN FORECASTING CURRENT AND FUTURE ELECTRICITY CONSUMPTION IN TÜRKİYE

Sergen Tümse

doi:10.17780/ksujes.1785928

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TÜRKİYE’DE GÜNCEL VE GELECEK ELEKTRİK TÜKETİMİNİN TAHMİNİNDE LSTM, XGBOOST VE RASTGELE ORMAN MODELLERİ

Öz

Bu çalışmada, 1 Ocak 2016'dan 31 Aralık 2024'e kadar toplam 3.287 günlük kayıttan oluşan veri setini kullanarak Türkiye'nin elektrik tüketimini tahmin etmek için karşılaştırmalı bir analiz uygulanmıştır ve her kayıt belirli bir ay için toplam elektrik tüketimini (MWh cinsinden) temsil etmektedir. XGBoost, rastgele orman (RF), uzun-kısa süreli bellek (LSTM) sinir ağları gibi üç farklı model oluşturulmuş ve birbirleriyle karşılaştırılmıştır. 2016-2022 yıllarına ait veriler (7 yıl) eğitim seti olarak kullanılırken, 2023-2024 yıl sonu (2 yıl) verileri ise test seti olarak ayrılmıştır. Daha sonra, 2025-2030 yılları arasındaki yıllar için Türkiye'deki elektrik tüketiminin geleceğe yönelik tahminleri gerçekleştirilmiştir. Oluşturulan modellerin doğruluğu, yaygın olarak kullanılan üç hata metriği ile değerlendirilmiştir: kök ortalama karekök hatası (RMSE), ortalama mutlak hata (MAE) ve ortalama mutlak yüzde hatası (MAPE). Sonuçlar, XGBoost'un 26.070,90 MWh RMSE, 16.071,54 MWh MAE ve %1,84 gibi oldukça düşük bir MAPE değeri ile en iyi sonuçları sağladığını ortaya koymuştur. Buna karşılık, RF ve LSTM yaklaşımları benzer performans göstermiş ve daha az doğru sonuçlar vermiştir. Örneğin, RF modeli 94297,89 MWh RMSE, 72301,67 MWh MAE ve %7,90 MAPE değeri elde ederken, LSTM tekniği 95115,75 MWh RMSE, 73335,54 MWh MAE ve %8,15 MAPE değeri elde etmiştir. Bu çalışmanın sonuçları, XGBoost yaklaşımının Türkiye'nin elektrik tüketimini modellemede güçlü bir performans gösterdiğini ortaya koymaktadır.

Anahtar Kelimeler

LSTM, XGBOOST AND RANDOM FOREST MODELS IN FORECASTING CURRENT AND FUTURE ELECTRICITY CONSUMPTION IN TÜRKİYE

Abstract

In this study, a comparative analysis was employed to predict electricity consumption of Türkiye using a dataset consisting of 3,287 daily records from January 1, 2016, to December 31, 2024, with each record representing the total electricity consumption (in MWh) for a specific day. Three different models, such as XGBoost, random forest (RF), and long-short term memory (LSTM) neural networks, were generated and compared with each other. Data from 2016 to 2022 (7 years) were used as the training set, while data from 2023 to the end of 2024 (2 years) were reserved as the test set. Subsequently, predictions of electricity consumption in Türkiye have been made for the years 2025–2030. The correctness of the generated models was assessed using three commonly used error metrics: root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results demonstrated that XGBoost yielded the most accurate outcomes, including an RMSE of 26,070.90 MWh, an MAE of 16,071.54 MWh, and a remarkably low MAPE of 1.84%. On the other hand, RF and LSTM techniques provided similar and less accurate results. For example, the RF approach yielded an RMSE of 94297.89 MWh, an MAE of 72301.67 MWh, and a MAPE of 7.90%, while LSTM model yielded an RMSE of 95115.75 MWh, an MAE of 73335.54 MWh, and a MAPE of 8.15%. The outcomes of this investigation reveal the strong performance of the XGBoost techniques in modeling Türkiye’s electricity consumption.

Keywords

Etik Beyan

Yazarlar herhangi bir çıkar çatışması olmadığını beyan etmektedirler.

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Makine Mühendisliğinde Sayısal Yöntemler

Bölüm

Araştırma Makalesi

Yazarlar

Sergen Tümse ^*
0000-0003-4764-747X
Türkiye

Yayımlanma Tarihi

3 Aralık 2025

Gönderilme Tarihi

17 Eylül 2025

Kabul Tarihi

1 Kasım 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1785928

IZ

https://izlik.org/JA84TX36TH

Kaynak Göster

RIS / Bibtex

APA

Tümse, S. (2025). LSTM, XGBOOST AND RANDOM FOREST MODELS IN FORECASTING CURRENT AND FUTURE ELECTRICITY CONSUMPTION IN TÜRKİYE. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 2139-2148. https://doi.org/10.17780/ksujes.1785928