Short-Term Load Forecasting: A Comprehensive Review and Simulation Study With CNN-LSTM Hybrids Approach

Short-term load forecasting (STLF) is vital in effectively managing the reserve requirement in modern power grids. Subsequently, it supports the grid operator in making effective and economical decisions during the power balancing operation. Therefore, this study comprehensively reviews STLF methods, including time series analysis, regression-based frameworks, artificial neural networks (ANNs), and hybrid models that employ different forecasting approaches. Detailed mathematical and graphical analyses and a comparative evaluation of these methods are provided, highlighting their advantages and disadvantages. Further, the study proposes a hybrid CNN-LSTM model comprised of Convolutional neural networks (CNN)…