Journal Article

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

Kilic, Heybet, Leonowicz, Zbigniew, Akhtar, Saima, Gono, Radomir, Ullah, Hafiz Sami, Jasiński, Michał, Zaheer, Asad, Shahzad, Sulman

2023

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / Alternative energy sources / Artificial intelligence / autoregression / Deep learning / Electric power systems / Electricity / Energy industry / Energy resources / Energy storage / Feature selection / Forecasting / Learning strategies / Machine learning / Neural networks / Performance evaluation / Renewable resources / short-term load forecasting / Time series / Trends