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These approaches overlook the potential insights gained from integrating data across different domains. This research addresses this gap using a cross\u2010domain dataset that includes building characteristics, energy usage, and environmental factors. Feature selection techniques, including filter methods (correlation, mutual information), wrapper methods (RFE), embedded methods (Lasso, Random Forest, and gradient boosting), and dimensionality reduction are used to identify the most significant features contributing to the energy efficiency of residential properties. These techniques identify the most significant features influencing energy consumption. The findings show that cross\u2010domain features like energy consumption, CO<jats:sub>2<\/jats:sub> emissions, and heating cost play a key role in predicting energy performance. By integrating data from multiple domains, the feature selection process reveals areas for energy optimization that are previously overlooked in single\u2010domain studies. The results provide valuable insights for energy consultants, building managers, and policymakers aiming to enhance energy efficiency in residential buildings. This research highlights the importance of cross\u2010domain data integration and offers a robust framework for feature selection. 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