Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Saima Akhtar, Sulman Shahzad (), Asad Zaheer, Hafiz Sami Ullah, Heybet Kilic, Radomir Gono (), Michał Jasiński and Zbigniew Leonowicz
Additional contact information
Saima Akhtar: Department of Computer Science, National Textile University, Faisalabad 37610, Pakistan
Sulman Shahzad: Department of Electrical Engineering, Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Asad Zaheer: Department of Electrical Engineering, NFC Institute of Engineering & Technology, Multan 60000, Pakistan
Hafiz Sami Ullah: National Transmission and Despatch Company Ltd., Lahore 54000, Pakistan
Heybet Kilic: Department of Electric Power and Energy Systems, Dicle University, 21280 Diyarbakır, Turkey
Radomir Gono: Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic
Michał Jasiński: Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Zbigniew Leonowicz: Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic

Energies, 2023, vol. 16, issue 10, 1-29

Abstract: 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.

Keywords: short-term load forecasting; neural networks; time series; autoregression; deep learning; artificial intelligence; support vector machines; hybrid models; exponential smoothing; data quality; random forest; decision tree; ensemble methods (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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