Visualization of Building Performance using Sankey Diagrams to Enhance the Decision-Making Process
Bibliographic record
Abstract
Nowadays, there are various building energy performance optimization methods available to designers. The aim of these methods is to vary building parameters to optimize the energy performance of the building in the early design stage and during operation and to choose the appropriate alternatives evaluated through multi-criteria objectives. However, current visualization methods have some limitations in evaluating simulation results in relation to non-performative or qualitative analysis. This paper investigates the feasibility of using Sankey diagrams to visualize and understand the upstream and downstream performance impacts of building design decisions. The current target audience is primarily architects and design engineers. The aim of this paper is to provide a workflow to obtain, analyze, and visualize energy flows obtained from simulation outputs. The developed workflow is applied to large office commercial reference building models that comply with the national energy code of Canada for buildings. Samples of Sankey diagrams are presented to visualize the impact of changing building/system components on the whole system performance and demonstrate energy-saving strategies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".