I. Introduction
Short-term load forecasting (STLF) K-means clustering to balance supply and demand, enhance energy management system (EMS) efficiency, and minimize power outages. It involves developing mathematical models based on historical load data to project future load values. Specifically, residential load forecasting focuses on predicting household power demand, which is crucial for effective energy management. Residential consumers’ behavior and preferences significantly influence power demand and innovation. Individual load forecasting enables estimating a user’s total power consumption, but it remains challenging due to the stochastic nature of a consumer’s power usage, introducing uncertainty in the timing of electricity-consuming activities. Additionally, the variable nature of electricity usage, even for a specific application of the same user, further complicates individual user load forecasting [1].