Energy Systems and Energy Sharing in Traditional and Sustainable Archetypes of Urban Developments
Bibliographic record
Abstract
Diverse factors influence the energy profile of an urban development including density, shape of buildings and their types, energy demand, and available energy resources. A systematic investigation of the energy characteristics of urban areas, involves the determination of representative archetypes of urban developments. This study presents a comparison of energy performance and resources between two categories of traditionally built urban development building clusters (BCs) in the North American urban context, and neighborhood units (NUs) designed with various sustainable principles and considerations. The study presents a methodology to optimize the mix of energy resources of individual building clusters and neighborhoods, as well as the optimization of energy sharing among the individual urban units of each category. Optimal energy sharing is determined based on the best combination of energy deficit and energy surplus of various clusters and neighborhoods. The study shows that in general neighborhood units encompassing diverse building uses and designed to allow different amenities within a walking distance perform better than commonly built building clusters with low usage diversity. Highly diverse neighborhoods that combine large commercial areas to high density residential buildings can generate up to 84% of their annual electrical and up to 37% of their annual thermal consumption. PV generation accounts for major part of the electrical energy generation of both individual urban units (BCs and NUs) and combination of these units. This can reach up to 92% of the total energy consumption of some combinations of NUs, while the remaining energy requirement is fulfilled by wind and waste to energy (3.4% and 4.9%, respectively). On the other hand, the study shows that thermal energy is mostly supplied by alternative energy sources, since building surfaces prioritize the accommodation of PV modules.
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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".