Accurate Electric Load Forecasting (ELF) is crucial for optimizing production capacity

improving operational efficiency

and managing energy resources effectively.Moreover

precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption

downtime

and waste.However

with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors

no single approach has emerged as universally effective.In response

this research presents a hybrid modeling framework that combines the strengths of Random Forest (RF) and Autoregressive Integrated Moving Average (ARIMA) models

enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy (MRMRMS) method—to produce a sparse model.Additionally

the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEOK)are used in this application.This methodology

termed SP-RF-ARIMA

is evaluated against existing approaches;it demonstrates more than 40% reduction in mean absolute error and root mean square error compared to the second-best method.