A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Accurate electrical load forecasting is critical for efficient power system operation, resource planning, and energy trading. This paper provides a comprehensive survey of load forecasting techniques, covering classical statistical methods (ARIMA, regression), machine learning approaches (ANN, SVM, random forests), deep learning methods (LSTM, CNNs), and hybrid models that combine multiple techniques. We analyze each approach in terms of forecasting accuracy, computational complexity, data requirements, and suitability for short-term, medium-term, and long-term forecasting horizons.

Recommended citation: A. A. Mamun, M. Sohel, N. Mohammad, M. S. Haque Sunny, D. R. Dipta and E. Hossain, "A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models," IEEE Access, vol. 8, pp. 134911-134939, 2020.