<![CDATA[Natural gas is often used for cooking, drying clothes, heating, etc., particularly in residential settings; it has been an essential component for human beings for many decades. This study proposes a hybrid ensemble regression machine learning model for forecasting residential natural gas demand. Accurate demand prediction tends to reduce energy waste and address some of theenergy challenges; such as the need for reliable, affordable, and sustainable energy consumption, thereby, improving energy management and resource planning. The proposed approach integrates multiple regression algorithms including K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Linear Regression (LR) to leverage thestrengths of each model to develop a hybrid model that enhances overall predictive performance. The ensemble method operates in two phases: training individual regression models on the dataset, followed by aggregating their predictions. Model performance is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R²), and prediction accuracy, and is benchmarked against individual models. Cross-validation techniques were applied to ensure therobustness of the results. Experimental results demonstrate that the hybrid ensemble approach consistently outperforms standalone models by capturing diverse patterns and relationships within the data.]]>
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