Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance
Improving energy efficiency in buildings is a challenge during operation and maintenance. The work proposes a cloud artificial intelligence-based building energy management (cloud AI-BEM) system for predicting building energy consumption. The proposed system includes the data layer, the AI-analytics...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-01-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2023.2223587 |
| _version_ | 1797336065064828928 |
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| author | Ngoc-Tri Ngo Ngoc-Son Truong Thi Thu Ha Truong Anh-Duc Pham Nhat-To Huynh |
| author_facet | Ngoc-Tri Ngo Ngoc-Son Truong Thi Thu Ha Truong Anh-Duc Pham Nhat-To Huynh |
| author_sort | Ngoc-Tri Ngo |
| collection | DOAJ |
| description | Improving energy efficiency in buildings is a challenge during operation and maintenance. The work proposes a cloud artificial intelligence-based building energy management (cloud AI-BEM) system for predicting building energy consumption. The proposed system includes the data layer, the AI-analytics layer, and the decision support information layer. The data layer collects and stores data in the cloud database management system. The analytics layer performs applied a hybrid AI model which was developed and deployed in this layer that enables predict future energy consumption in buildings. The hybrid AI model, namely the SAMFOR model was developed based on the integration of the seasonal autoregressive integrated moving average (SARIMA) model and the firefly algorithm (FA) and least-squares support vector regression (LSSVR). The web-based layer visualizes insights for users. As insights, the cloud AI-BEM system enables to monitor and to compare the energy consumption among buildings; to predict one-day-ahead energy use in buildings, to produce key performance indicators of energy use; to visualize energy data and outdoor temperature data; to easily interact with data from the system interface. Average accuracy in terms of RMSE values ranged from 1.36 kW per 30 min. The R values were higher than 0.957 and very close to 1. |
| first_indexed | 2024-03-08T08:48:45Z |
| format | Article |
| id | doaj.art-d5820e897dd248169807cfd8fece4013 |
| institution | Directory Open Access Journal |
| issn | 1347-2852 |
| language | English |
| last_indexed | 2024-03-08T08:48:45Z |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj.art-d5820e897dd248169807cfd8fece40132024-02-01T14:39:33ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522024-01-0123126428110.1080/13467581.2023.22235872223587Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performanceNgoc-Tri Ngo0Ngoc-Son Truong1Thi Thu Ha Truong2Anh-Duc Pham3Nhat-To Huynh4The University of Danang - University of Science and TechnologyThe University of Danang - University of Science and TechnologyThe University of Danang – University of Technology and EducationThe University of Danang - University of Science and TechnologyThe University of Danang - University of Science and TechnologyImproving energy efficiency in buildings is a challenge during operation and maintenance. The work proposes a cloud artificial intelligence-based building energy management (cloud AI-BEM) system for predicting building energy consumption. The proposed system includes the data layer, the AI-analytics layer, and the decision support information layer. The data layer collects and stores data in the cloud database management system. The analytics layer performs applied a hybrid AI model which was developed and deployed in this layer that enables predict future energy consumption in buildings. The hybrid AI model, namely the SAMFOR model was developed based on the integration of the seasonal autoregressive integrated moving average (SARIMA) model and the firefly algorithm (FA) and least-squares support vector regression (LSSVR). The web-based layer visualizes insights for users. As insights, the cloud AI-BEM system enables to monitor and to compare the energy consumption among buildings; to predict one-day-ahead energy use in buildings, to produce key performance indicators of energy use; to visualize energy data and outdoor temperature data; to easily interact with data from the system interface. Average accuracy in terms of RMSE values ranged from 1.36 kW per 30 min. The R values were higher than 0.957 and very close to 1.http://dx.doi.org/10.1080/13467581.2023.2223587data-driven analyticsbuilding energy efficiencyartificial intelligencemachine learningbuilding energy management system |
| spellingShingle | Ngoc-Tri Ngo Ngoc-Son Truong Thi Thu Ha Truong Anh-Duc Pham Nhat-To Huynh Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance Journal of Asian Architecture and Building Engineering data-driven analytics building energy efficiency artificial intelligence machine learning building energy management system |
| title | Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance |
| title_full | Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance |
| title_fullStr | Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance |
| title_full_unstemmed | Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance |
| title_short | Implementing a web-based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance |
| title_sort | implementing a web based optimized artificial intelligence system with metaheuristic optimization for improving building energy performance |
| topic | data-driven analytics building energy efficiency artificial intelligence machine learning building energy management system |
| url | http://dx.doi.org/10.1080/13467581.2023.2223587 |
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