Novel Energy System Design Workflow for Zero-Carbon Energy District Development
Bibliographic record
Abstract
The growing urban population globally leads to higher greenhouse gas (GHG) emissions and stress on the electricity networks for meeting the increasing demand. In the early urban design stages, the optimization of the urban morphology and building physics characteristics can reduce energy demand. Local generation using renewable energy resources is also a viable option to reduce emissions and improve grid reliability. Notwithstanding, energy simulation and environmental impact assessment of urban building design strategies are usually not done until the execution planning stage. To address this research gap, a novel framework for designing energy systems for zero-carbon districts is developed. An urban building energy model is integrated with an urban energy system model in this framework. Dynamic prediction of heating and cooling demand and automatic sizing of different energy system configurations based on the calculated demands are the framework's primary capabilities. The workability of the framework has been tested on a case study for an urban area in Montreal to design and compare two different renewable energy systems comprising photovoltaic panels (PV), air-source, and ground source heat pumps. The case study results show that the urban building energy model could successfully predict the heating and cooling demands in multiple spatiotemporal resolutions, while the urban energy system model provides system solutions for achieving a zero-carbon or positive energy district.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".