Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy Efficiency
Energy efficiency is a critical concern in built environment. Identifying key features that drive energy consumption is essential for optimizing building performance. Traditionally, studies have focused on single‐domain datasets. These approaches overlook the potential insights gained from integrati...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400939 |
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| _version_ | 1849230104523177984 |
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| author | Hafiz Muhammad Shakeel Shamaila Iram Hafiz Muhammad Athar Farid Richard Hill |
| author_facet | Hafiz Muhammad Shakeel Shamaila Iram Hafiz Muhammad Athar Farid Richard Hill |
| author_sort | Hafiz Muhammad Shakeel |
| collection | DOAJ |
| description | Energy efficiency is a critical concern in built environment. Identifying key features that drive energy consumption is essential for optimizing building performance. Traditionally, studies have focused on single‐domain datasets. These approaches overlook the potential insights gained from integrating data across different domains. This research addresses this gap using a cross‐domain 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‐domain features like energy consumption, CO2 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‐domain 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‐domain data integration and offers a robust framework for feature selection. Ultimately, it contributes to more effectiveenergy‐saving strategies and sustainable building practices. |
| format | Article |
| id | doaj-art-a7ebad88f7784b7b8f18123a0d67bb5d |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-a7ebad88f7784b7b8f18123a0d67bb5d2025-08-21T11:05:47ZengWileyAdvanced Intelligent Systems2640-45672025-08-0178n/an/a10.1002/aisy.202400939Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy EfficiencyHafiz Muhammad Shakeel0Shamaila Iram1Hafiz Muhammad Athar Farid2Richard Hill3Department of Computer Science University of Huddersfield Huddersfield HD1 3DH UKDepartment of Computer Science University of Huddersfield Huddersfield HD1 3DH UKDepartment of Computer Science University of Huddersfield Huddersfield HD1 3DH UKDepartment of Computer Science University of Huddersfield Huddersfield HD1 3DH UKEnergy efficiency is a critical concern in built environment. Identifying key features that drive energy consumption is essential for optimizing building performance. Traditionally, studies have focused on single‐domain datasets. These approaches overlook the potential insights gained from integrating data across different domains. This research addresses this gap using a cross‐domain 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‐domain features like energy consumption, CO2 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‐domain 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‐domain data integration and offers a robust framework for feature selection. Ultimately, it contributes to more effectiveenergy‐saving strategies and sustainable building practices.https://doi.org/10.1002/aisy.202400939buildings featuresdimensionality reductionenergy efficiencyenergy performancefeature selectionresidential buildings |
| spellingShingle | Hafiz Muhammad Shakeel Shamaila Iram Hafiz Muhammad Athar Farid Richard Hill Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy Efficiency Advanced Intelligent Systems buildings features dimensionality reduction energy efficiency energy performance feature selection residential buildings |
| title | Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy Efficiency |
| title_full | Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy Efficiency |
| title_fullStr | Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy Efficiency |
| title_full_unstemmed | Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy Efficiency |
| title_short | Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy Efficiency |
| title_sort | linear and tree based intelligent investigation of cross domain housing features to enhance energy efficiency |
| topic | buildings features dimensionality reduction energy efficiency energy performance feature selection residential buildings |
| url | https://doi.org/10.1002/aisy.202400939 |
| work_keys_str_mv | AT hafizmuhammadshakeel linearandtreebasedintelligentinvestigationofcrossdomainhousingfeaturestoenhanceenergyefficiency AT shamailairam linearandtreebasedintelligentinvestigationofcrossdomainhousingfeaturestoenhanceenergyefficiency AT hafizmuhammadatharfarid linearandtreebasedintelligentinvestigationofcrossdomainhousingfeaturestoenhanceenergyefficiency AT richardhill linearandtreebasedintelligentinvestigationofcrossdomainhousingfeaturestoenhanceenergyefficiency |