AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm
AI-based classification models for low-energy buildings are essential in promoting the economic development of smart cities by optimizing energy use, reducing operational costs, and enhancing overall efficiency. These models classify buildings based on energy consumption patterns, predicting energy...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10844090/ |
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| author | Lin Yang |
| author_facet | Lin Yang |
| author_sort | Lin Yang |
| collection | DOAJ |
| description | AI-based classification models for low-energy buildings are essential in promoting the economic development of smart cities by optimizing energy use, reducing operational costs, and enhancing overall efficiency. These models classify buildings based on energy consumption patterns, predicting energy needs and identifying areas for improvement. The spherical fuzzy set (SFS) is a potent approach used to handle ambiguous information during decision analysis and integrate big expert’s opinions. This article evaluates intelligent AI-based models for low-energy buildings and economic development under some specific characteristics or key features. For this purpose, we modified the theory of criteria importance through intercriteria correlation (CRITIC) and combined compromise solution (COCOSO) methods to assess suitable optimal options for the multi-attribute group decision-making (MAGDM) problem. Moreover, we also propose a novel decision algorithm of the CRITIC-COCOSO method under consideration of spherical fuzzy situations. The CRITIC-COCOSO method has great capabilities to investigate weights of criterion and accurate ranking of preferences under specific linguistic scales. An experimental case study is established to investigate a reliable AI-based advanced technology and prove the superiority of diagnosed theories. Additionally, a robust comparison method is used to prove the supremacy of derived theories and pioneered mathematical terminologies. In the end, some key features and remarkable comments are also discussed. |
| format | Article |
| id | doaj-art-7d44ca01f2c44d189748c674961d8153 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7d44ca01f2c44d189748c674961d81532025-01-31T00:01:15ZengIEEEIEEE Access2169-35362025-01-0113183861840210.1109/ACCESS.2025.353096510844090AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision AlgorithmLin Yang0https://orcid.org/0009-0009-9403-1150School of Architecture and Urban Planning, Qingdao University of Technology, Qingdao, Shandong, ChinaAI-based classification models for low-energy buildings are essential in promoting the economic development of smart cities by optimizing energy use, reducing operational costs, and enhancing overall efficiency. These models classify buildings based on energy consumption patterns, predicting energy needs and identifying areas for improvement. The spherical fuzzy set (SFS) is a potent approach used to handle ambiguous information during decision analysis and integrate big expert’s opinions. This article evaluates intelligent AI-based models for low-energy buildings and economic development under some specific characteristics or key features. For this purpose, we modified the theory of criteria importance through intercriteria correlation (CRITIC) and combined compromise solution (COCOSO) methods to assess suitable optimal options for the multi-attribute group decision-making (MAGDM) problem. Moreover, we also propose a novel decision algorithm of the CRITIC-COCOSO method under consideration of spherical fuzzy situations. The CRITIC-COCOSO method has great capabilities to investigate weights of criterion and accurate ranking of preferences under specific linguistic scales. An experimental case study is established to investigate a reliable AI-based advanced technology and prove the superiority of diagnosed theories. Additionally, a robust comparison method is used to prove the supremacy of derived theories and pioneered mathematical terminologies. In the end, some key features and remarkable comments are also discussed.https://ieeexplore.ieee.org/document/10844090/Spherical fuzzy informationSchweizer-Sklar operatorsCRITIC-COCOSO method and decision-making process |
| spellingShingle | Lin Yang AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm IEEE Access Spherical fuzzy information Schweizer-Sklar operators CRITIC-COCOSO method and decision-making process |
| title | AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm |
| title_full | AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm |
| title_fullStr | AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm |
| title_full_unstemmed | AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm |
| title_short | AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm |
| title_sort | ai based classification model for low energy buildings promoting sustainable economic development of smart cities with spherical fuzzy decision algorithm |
| topic | Spherical fuzzy information Schweizer-Sklar operators CRITIC-COCOSO method and decision-making process |
| url | https://ieeexplore.ieee.org/document/10844090/ |
| work_keys_str_mv | AT linyang aibasedclassificationmodelforlowenergybuildingspromotingsustainableeconomicdevelopmentofsmartcitieswithsphericalfuzzydecisionalgorithm |