A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings
Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid mac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/5/1167 |
| _version_ | 1797600799751143424 |
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| author | Yu Gong Erzsébet Szeréna Zoltán János Gyergyák |
| author_facet | Yu Gong Erzsébet Szeréna Zoltán János Gyergyák |
| author_sort | Yu Gong |
| collection | DOAJ |
| description | Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid machine learning that copes with the drawbacks of single methods. In this work, an optimized version of a popular machine learning model, namely feed-forward neural network (FFNN) is used for simultaneously predicting annual thermal energy demand (ATED) and annual weighted average discomfort degree-hours (WADDH) by analyzing eleven input factors that represent the building circumstances. The optimization task is carried out by a multi-tracker optimization algorithm (MTOA) which is a powerful metaheuristic algorithm. Moreover, three benchmark algorithms including the slime mould algorithm (SMA), seeker optimization algorithm (SOA), and vortex search algorithm (VSA) perform the same task for comparison purposes. The accuracy of the models is assessed using error and correlation indicators. Based on the results, the MTOA (with root mean square errors 2.48 and 5.88, along with Pearson correlation coefficients 0.995 and 0.998 for the ATED and WADHH, respectively) outperformed the benchmark techniques in learning the energy behavior of the building. This algorithm could optimize 100 internal variables of the FFNN and acquire the trend of ATED and WADHH with excellent accuracy. Despite different rankings of the four algorithms in the prediction phase, the MTOA (with root mean square errors 9.84 and 95.96, along with Pearson correlation coefficients 0.972 and 0.997 for the ATED and WADHH, respectively) was still among the best, and altogether, the hybrid of FFNN-MTOA is recommended for promising applications of building energy analysis in real-world projects. |
| first_indexed | 2024-03-11T03:53:03Z |
| format | Article |
| id | doaj.art-bcf158308ac24d16a1ce30925cd21ca2 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T03:53:03Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-bcf158308ac24d16a1ce30925cd21ca22023-11-18T00:44:29ZengMDPI AGBuildings2075-53092023-04-01135116710.3390/buildings13051167A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential BuildingsYu Gong0Erzsébet Szeréna Zoltán1János Gyergyák2Marcel Breuer Doctoral School, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány u. 2, H-7624 Pécs, HungaryDepartment of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány u. 2, H-7624 Pécs, HungaryDepartment of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány u. 2, H-7624 Pécs, HungaryEnergy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid machine learning that copes with the drawbacks of single methods. In this work, an optimized version of a popular machine learning model, namely feed-forward neural network (FFNN) is used for simultaneously predicting annual thermal energy demand (ATED) and annual weighted average discomfort degree-hours (WADDH) by analyzing eleven input factors that represent the building circumstances. The optimization task is carried out by a multi-tracker optimization algorithm (MTOA) which is a powerful metaheuristic algorithm. Moreover, three benchmark algorithms including the slime mould algorithm (SMA), seeker optimization algorithm (SOA), and vortex search algorithm (VSA) perform the same task for comparison purposes. The accuracy of the models is assessed using error and correlation indicators. Based on the results, the MTOA (with root mean square errors 2.48 and 5.88, along with Pearson correlation coefficients 0.995 and 0.998 for the ATED and WADHH, respectively) outperformed the benchmark techniques in learning the energy behavior of the building. This algorithm could optimize 100 internal variables of the FFNN and acquire the trend of ATED and WADHH with excellent accuracy. Despite different rankings of the four algorithms in the prediction phase, the MTOA (with root mean square errors 9.84 and 95.96, along with Pearson correlation coefficients 0.972 and 0.997 for the ATED and WADHH, respectively) was still among the best, and altogether, the hybrid of FFNN-MTOA is recommended for promising applications of building energy analysis in real-world projects.https://www.mdpi.com/2075-5309/13/5/1167sustainable energymachine learningestimationbuilding thermal loadMTOA optimization |
| spellingShingle | Yu Gong Erzsébet Szeréna Zoltán János Gyergyák A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings Buildings sustainable energy machine learning estimation building thermal load MTOA optimization |
| title | A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings |
| title_full | A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings |
| title_fullStr | A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings |
| title_full_unstemmed | A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings |
| title_short | A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings |
| title_sort | neural network trained by multi tracker optimization algorithm applied to energy performance estimation of residential buildings |
| topic | sustainable energy machine learning estimation building thermal load MTOA optimization |
| url | https://www.mdpi.com/2075-5309/13/5/1167 |
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