Faisul Arif Ahmad

Abstract Short-term load forecasting (STLF) plays a critical role in ensuring the reliable and economical operation of power systems, particularly under complex and dynamic meteorological conditions. While Deep Residual Networks (DRNs) have demonstrated strong capability in modeling nonlinear load patterns, existing studies have predominantly focused on architectural enhancements, with limited attention given to input-level feature processing.

Abstract The impact of network depth on the performance of deep residual networks (DRNs) for short-term load forecasting (STLF) remains insufficiently understood. This study conducts a systematic empirical investigation of network depth within DRN-based frameworks, with a particular focus on its relationship with forecasting performance, model behavior, and dataset characteristics.

Abstract Modern electrical systems rely on short-term load forecasting (STLF) for reliable and efficient operation. Although deep residual networks (DRNs) have achieved strong forecasting performance in STLF, most existing studies mainly emphasize architectural modifications, while the influence of training strategies has received relatively limited attention. In particular, the effect of training schedule design, especially epoch allocation, remains insufficiently studied.