<![CDATA[This research investigates how to accurately predict electrical energy consumption to address growing global energy demands. The study employs three Machine Learning (ML) models: k-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost. To enhance prediction accuracy, the researchers included a data pre-processing step using min-max normalization. The analysis utilized a dataset containing 52,416 records of power consumption from Tetouan City. The dataset was divided into training and testing sets using different ratios (90:10, 80:20, 50:50) to evaluate model performance. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to assess prediction accuracy. Min-max normalization significantly improved KNN's performance (reduced RMSE and MAPE). RF achieved similar accuracy with and without normalization. CatBoost also demonstrated stable performance regardless of normalization. Data pre-processing, specifically min-max normalization, is crucial for improving the accuracy of distance-based algorithms like KNN. Decision tree-based algorithms like RF and CatBoost are less sensitive to data normalization. These findings emphasize the importance of selecting appropriate pre-processing techniques to optimize energy consumption prediction models, which can contribute to better energy management strategies.]]>
