Mohd Azim Zainal

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.

Kolej Pengajian Pengkomputeran, Informatik dan Matematik (KPPIM) UiTM Cawangan Pahang (UCPh) telah menganjurkan sebuah program yang diberi nama TECHXPERIENCE DAY pada 1 Mei 2024 yang lalu.

Program “Automasi dan Pengoptimuman Sistem Penjadualan (Scheduling Automation and Optimization)” yang berbentuk taklimat pengurusan pentadbiran dan perkongsian ilmu telah berlangsung selama 2 jam bermula jam 3.00 petang sehingga 5.00 petang pada 29 Februari 2024 (Khamis) secara atas talian dengan menggunakan aplikasi Webex.