Predicting world electricity generation by sources using different machine learning algorithms
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/restrictedAccess
Özet
Electrical energy plays a crucial role in both social and economic growth. It is thought to be an essential part of industrial manufacturing. In addition to its contribution to industry, electrical energy is essential for addressing the needs of people on a daily basis. Therefore, electricity generation prediction is crucial for accurate electricity planning and energy usage, with machine learning (ML) algorithms becoming popular for their ability to extract complex relationships and make precise predictions. With the data from the period 2000-2022, this study predicts world electricity generation for 2023 by different energy sources employing seven different ML algorithms, namely long short-term memory (LSTM), artificial neural network (ANN), linear regression (LR), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR) and eXtreme gradient boosting (XGBoost). The algorithms were also contrasted in the study, and it was discovered that LSTM produced the most accurate predictions. [Received: June 16, 2023; Accepted: August 19, 2023]
Açıklama
Anahtar Kelimeler
Energy, Electricity generation, Machine learning, Prediction, Long short-term memory, LSTM, Artificial neural network, ANN, Support vector regression, SVR, Decision tree regression, DTR, Random forest regression, RFR
Kaynak
International Journal of Oil, Gas and Coal Technology
WoS Q Değeri
Scopus Q Değeri
Cilt
35
Sayı
1
Künye
Özdemir, Mehmet H., Aylak, Batin L., Oral, O., İnce, M. (2024). Predicting world electricity generation by sources using different machine learning algorithms. International Journal of Oil, Gas and Coal Technology, 35 (1), 98-115.
