Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Related Variables
Machine learning models have become a potential alternative for building energy performance studies since they provide fast and reliable prediction results. However, decisions in the modeling process are sometimes made without knowing their possible impact on the results, which may lead to unstable...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/8/1301 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850183655762165760 |
|---|---|
| author | Damla Kömürcü Ecem Edis |
| author_facet | Damla Kömürcü Ecem Edis |
| author_sort | Damla Kömürcü |
| collection | DOAJ |
| description | Machine learning models have become a potential alternative for building energy performance studies since they provide fast and reliable prediction results. However, decisions in the modeling process are sometimes made without knowing their possible impact on the results, which may lead to unstable process management. Therefore, this study aims to obtain a machine learning modeling process framework focusing on critical-decision subjects through a systematic review of the recent literature. Preferences of the current supervised modeling practices on process-related variables to have prediction models with high accuracy were analyzed in the studies using simulation data. In this paper, a general framework of the processes is presented through their steps and decision subjects. Regarding these steps, the frequency of the methods used, strategies followed against the limitations, common sources of concerns, and intertwined workflows are analyzed with their effects on prediction performance in terms of accuracy. In addition, correlations between process-related variables, i.e., decision subjects and model performance, are investigated to quantify the impacts. As a result, the most effective decision subjects on accuracy were observed as the machine learning algorithm to be used, input variables to be included, and the range of the sample size, respectively. |
| format | Article |
| id | doaj-art-a51537b5eb474bccad94f61648b0f8d1 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-a51537b5eb474bccad94f61648b0f8d12025-08-20T02:17:19ZengMDPI AGBuildings2075-53092025-04-01158130110.3390/buildings15081301Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Related VariablesDamla Kömürcü0Ecem Edis1Construction Sciences Ph.D. Program, Graduate School, Istanbul Technical University, 34469 Istanbul, TürkiyeDepartment of Architecture, Faculty of Architecture, Istanbul Technical University, 34367 Istanbul, TürkiyeMachine learning models have become a potential alternative for building energy performance studies since they provide fast and reliable prediction results. However, decisions in the modeling process are sometimes made without knowing their possible impact on the results, which may lead to unstable process management. Therefore, this study aims to obtain a machine learning modeling process framework focusing on critical-decision subjects through a systematic review of the recent literature. Preferences of the current supervised modeling practices on process-related variables to have prediction models with high accuracy were analyzed in the studies using simulation data. In this paper, a general framework of the processes is presented through their steps and decision subjects. Regarding these steps, the frequency of the methods used, strategies followed against the limitations, common sources of concerns, and intertwined workflows are analyzed with their effects on prediction performance in terms of accuracy. In addition, correlations between process-related variables, i.e., decision subjects and model performance, are investigated to quantify the impacts. As a result, the most effective decision subjects on accuracy were observed as the machine learning algorithm to be used, input variables to be included, and the range of the sample size, respectively.https://www.mdpi.com/2075-5309/15/8/1301supervised machine learningbuilding energy performanceearly design stageenergy prediction modeling processinput–output relations |
| spellingShingle | Damla Kömürcü Ecem Edis Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Related Variables Buildings supervised machine learning building energy performance early design stage energy prediction modeling process input–output relations |
| title | Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Related Variables |
| title_full | Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Related Variables |
| title_fullStr | Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Related Variables |
| title_full_unstemmed | Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Related Variables |
| title_short | Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Related Variables |
| title_sort | machine learning modeling for building energy performance prediction based on simulation data a systematic review of the processes performances and correlation of process related variables |
| topic | supervised machine learning building energy performance early design stage energy prediction modeling process input–output relations |
| url | https://www.mdpi.com/2075-5309/15/8/1301 |
| work_keys_str_mv | AT damlakomurcu machinelearningmodelingforbuildingenergyperformancepredictionbasedonsimulationdataasystematicreviewoftheprocessesperformancesandcorrelationofprocessrelatedvariables AT ecemedis machinelearningmodelingforbuildingenergyperformancepredictionbasedonsimulationdataasystematicreviewoftheprocessesperformancesandcorrelationofprocessrelatedvariables |