Large-scale building simulation using cloud computing for estimating lifecycle energy consumption
Bibliographic record
Abstract
The use of whole building simulation is increasing to support the design process. Often it is desirable to evaluate many scenarios, however the simulation time involved presents a significant barrier. Simulationists are forced to reduce the number of scenarios evaluated to meet time constraints. With cloud computing, simulationists can significantly reduce the total simulation time by allocating portions of the simulations to multiple processor cores. The benefit of cloud computing is demonstrated through a case study project, which computes the lifecycle energy consumption (LEC) of 1 080 000 single detached home design scenarios in Toronto, for a budget of CAN$2400. Code was written using Python to couple EnergyPlus and ATHENA IE to modify input files, process results and calculate LEC. The results of this study suggest that utilizing cloud computing to simulate large scenario studies represents an efficient method that is beginning to surface in mainstream building simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".