BIM based Energy Consumption Estimation using Data-driven model
Bibliographic record
Abstract
Building Information Modeling (BIM) is undergoing rapid technological evolution in the building construction industry. Recently, employing BIM as a building 3D digital model in Building Energy Consumption Estimation (BECE) has gained momentum because of the enriched geometric and semantic information. Indeed, indoor BECE notably depends on the semantics, geometry (building elements and shapes), and topology information of the building's elements to recognize the spaces in a building with high energy demand. However, despite extensive studies on applying the BIM and Industry Foundation Classes (IFC) as an open standard data model for BIM in BECE analysis, employing the full potential of the BIM remains poor due to its data model complexity and incompatibility with BECE data-driven algorithms. There is a significant lack of building energy modeling in using the detailed geometry, semantic, and 3D topology information in BECE data-driven models. The objective of this dissertation is to develop an innovative and comprehensive framework called space-based precise building energy consumption estimation using BIM. In this research, a framework is developed to convert the IFC model into a space-based graph, including the geometry, semantic, and topology information on the proposed graph nodes and edges. The graph is compatible with the machine learning algorithm. A graph-based classification algorithm is suggested in this research to find critical spaces in the building for energy consumption. This research proposed a prescriptive model by integrating building energy simulation with optimization techniques, using BIM data and a Genetic Algorithm (GA) to develop a prescriptive model for indoor building design. The study focuses on space-based BECE analysis, leveraging BIM interoperability to recommend optimal solutions. The proposed model employs the value engineering method to balance energy consumption, functionality, and cost, providing engineers and designers with insights to optimize building performance effectively. This approach enhances energy efficiency and offers substantial design optimization solutions, bridging the gap between energy prediction and practical application in the architectural, engineering, and construction (AEC) industry. The outcomes of this study are conducive to contemporary data-driven models in BIM and indoor BECE analysis. This provides a comprehensive perspective on both present and prospective requirements for BIM in the estimation of building energy consumption. The study integrates various sectors, including architecture, construction, machine learning, ad 3D geospatial analysis, aiming to derive comprehensive and optimal solutions. Furthermore, it underscores the necessity for future multidisciplinary research by unfolding existing gaps and limitations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".