Optimal Device Sizing for Zero Energy Buildings: Sensitivity of Nonlinear Model to Uncertainties
Bibliographic record
Abstract
Buildings, as one of the major final energy consumers, are among key contributors to greenhouse gas emissions. A zero energy building is, by definition, a building that produces as much energy from renewable sources as it consumes yearly. In this paper, we propose a comprehensive device sizing model to find the most cost-optimal size of thermal and electrical devices in a zero energy building. The presence of several technologies (i.e., photovoltaic panel, solar thermal collector, heat pump, combined heat and power, heat storage tank, and battery energy storage) and their practical nonlinear behavior are considered in the proposed model. Then, to investigate the effect of uncertainties (i.e., demand and weather forecasting errors) in the quality of the optimal solution, a sensitivity analysis with respect to the correlation between uncertainties is performed. Finally, to illustrate the advantages of the proposed model, a typical building located on the Vancouver campus of the University of British Columbia is studied.
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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".