Short-term load forecasting models: A review of challenges, progress, and the road ahead
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
MDPI
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.
Açıklama
Anahtar Kelimeler
Artificial intelligence, Autoregression, Data quality, Decision tree, Deep learning, Ensemble methods, Exponential smoothing, Hybrid models, Neural networks, Random forest, Short-term load forecasting, Support vector machines, Time series
Kaynak
Energies
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
16
Sayı
10
Künye
Akhtar, S., Shahzad, S., Zaheer, A., Ullah, H. S., Kılıç, H., Gono, R. ve diğerleri. (2023). Short-term load forecasting models: A review of challenges, progress, and the road ahead. Energies, 16(10), 1-29.
