Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case Study
The energy performance prediction of buildings plays a significant role in the design phases. Theoretical analysis and statistical analysis are typically carried out to predict energy consumption. However, due to the complexity of the building characteristics, precise energy performance can hardly b...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/12/4/449 |
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| author | Faham Tahmasebinia Ruifeng Jiang Samad Sepasgozar Jinlin Wei Yilin Ding Hongyi Ma |
| author_facet | Faham Tahmasebinia Ruifeng Jiang Samad Sepasgozar Jinlin Wei Yilin Ding Hongyi Ma |
| author_sort | Faham Tahmasebinia |
| collection | DOAJ |
| description | The energy performance prediction of buildings plays a significant role in the design phases. Theoretical analysis and statistical analysis are typically carried out to predict energy consumption. However, due to the complexity of the building characteristics, precise energy performance can hardly be predicted in the early design stage. This study considers both building information modeling (BIM) and statistical approaches, including several regression models for the prediction purpose. This paper also highlights a number of findings of energy modeling related to building energy performance simulation software, particularly Autodesk Green Building Studio. In this research, the geometric models were created using Autodesk Revit. Based on the energy simulation conducted by Autodesk Green Building Studio (GBS), the energy properties of five prototype and case study models were determined. The GBS simulation was carried out using DOE 2.2 engine. Eight parameters were used in BIM, including building type, location, building area, analysis year, floor-to-ceiling height, floor construction, wall construction, and ceiling construction. The Monte Carlo simulation method was performed to predict precise energy consumption. Among the regression models developed, the single variable linear regression models appear to have high accuracy. Although there exist some limitations in applying the equation in EUI prediction, the rough estimation of energy use was realized. Regression model validation was carried out using the model from the case study and Monte Carlo simulation results. A total of 35 runs of validation were performed, and most differences were maintained within 5%. The results show some limitations in the application of the linear regression model. |
| first_indexed | 2024-03-09T11:04:11Z |
| format | Article |
| id | doaj.art-63878c8984044b65a44f104eecb94686 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T11:04:11Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-63878c8984044b65a44f104eecb946862023-12-01T01:02:31ZengMDPI AGBuildings2075-53092022-04-0112444910.3390/buildings12040449Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case StudyFaham Tahmasebinia0Ruifeng Jiang1Samad Sepasgozar2Jinlin Wei3Yilin Ding4Hongyi Ma5School of Civil Engineering, The University of Sydney, Sydney, NSW 2006, AustraliaSchool of Civil Engineering, The University of Sydney, Sydney, NSW 2006, AustraliaSchool of Built Environment, University of New South Wales, Sydney, NSW 2052, AustraliaSchool of Civil Engineering, The University of Sydney, Sydney, NSW 2006, AustraliaSchool of Civil Engineering, The University of Sydney, Sydney, NSW 2006, AustraliaSchool of Built Environment, University of New South Wales, Sydney, NSW 2052, AustraliaThe energy performance prediction of buildings plays a significant role in the design phases. Theoretical analysis and statistical analysis are typically carried out to predict energy consumption. However, due to the complexity of the building characteristics, precise energy performance can hardly be predicted in the early design stage. This study considers both building information modeling (BIM) and statistical approaches, including several regression models for the prediction purpose. This paper also highlights a number of findings of energy modeling related to building energy performance simulation software, particularly Autodesk Green Building Studio. In this research, the geometric models were created using Autodesk Revit. Based on the energy simulation conducted by Autodesk Green Building Studio (GBS), the energy properties of five prototype and case study models were determined. The GBS simulation was carried out using DOE 2.2 engine. Eight parameters were used in BIM, including building type, location, building area, analysis year, floor-to-ceiling height, floor construction, wall construction, and ceiling construction. The Monte Carlo simulation method was performed to predict precise energy consumption. Among the regression models developed, the single variable linear regression models appear to have high accuracy. Although there exist some limitations in applying the equation in EUI prediction, the rough estimation of energy use was realized. Regression model validation was carried out using the model from the case study and Monte Carlo simulation results. A total of 35 runs of validation were performed, and most differences were maintained within 5%. The results show some limitations in the application of the linear regression model.https://www.mdpi.com/2075-5309/12/4/449Green BuildingBIMregression analysisMonte Carlo simulationbuilding energy performanceDOE-2 simulation |
| spellingShingle | Faham Tahmasebinia Ruifeng Jiang Samad Sepasgozar Jinlin Wei Yilin Ding Hongyi Ma Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case Study Buildings Green Building BIM regression analysis Monte Carlo simulation building energy performance DOE-2 simulation |
| title | Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case Study |
| title_full | Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case Study |
| title_fullStr | Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case Study |
| title_full_unstemmed | Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case Study |
| title_short | Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case Study |
| title_sort | implementation of bim energy analysis and monte carlo simulation for estimating building energy performance based on regression approach a case study |
| topic | Green Building BIM regression analysis Monte Carlo simulation building energy performance DOE-2 simulation |
| url | https://www.mdpi.com/2075-5309/12/4/449 |
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