Long-term Electricity Demand Forecasting using THI-ConvNet

Electricity load forecasting is critical for ensuring the stability and efficiency of power supply systems, particularly during holidays when demand patterns tend to deviate from normal trends. However, most existing studies focus on short-term forecasts and lack the capability to effectively incorporate the irregularities caused by long-term holidays. To address this gap, we propose a novel deep learning-based forecasting model called the Temporal Holiday-Integrated (THI) model, which is the first to explicitly integrate both short-term and long-term holiday effects into electricity demand prediction. The model is trained using daily data collected from January 1, 2013, to December 31, 2024 (12 years in total), consisting of 61 meteorological variables from the Korea Meteorological Administration, 1 national holiday indicator, and 1 electricity transaction volume column.