Investigation of the Design Parameters and Energy Sharing Strategies for Sustainable Urban Infrastructure
Bibliographic record
Abstract
Incorporating energy efficiency measures, on-site renewable energy generation, and energy sharing has been shown to reduce the energy consumption of neighborhoods. In addition, integrating agricultural greenhouses within the planning process of neighborhoods can assist in carbon capturing and avoiding emissions related to food growth and transportation while providing a certain layer of food resilience. The current study comprises a comprehensive investigation of key parameters of a neighborhood composed of residential buildings, retail building, and agricultural greenhouses. Key findings and recommendations related to energy efficiency measures, on-site renewable energy generation, and energy sharing are presented. The design parameters studied include building envelope, building mechanical and electrical systems, and control systems. Various neighborhood building layouts and building configurations have been studied. The density effect on energy sharing has been analyzed as well. Waste energy recovery and sharing methodology have been developed and applied to a variety of neighborhood types, configurations, and densities. The analysis employs the EnergyPlus and EnergyPlus-DesignBuilder co-simulation platforms to simulate configurations consisting of a combination of design parameters, heating and cooling demand/consumption of neighborhood buildings, energy sharing, and greenhouse gas emission reduction, relative to the base-case reference design. The weather data for Calgary, Canada (51°N) are employed to represent a northern, cold climate zone. A holistic design methodology is developed to support the design and analysis of the energy sharing methodology of a mixed-use neighborhood. This methodology may be employed to assist the design of high energy efficient and low carbon footprint neighborhoods and help food security by using integrated greenhouses to capture carbon and grow food locally.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".