Analysis of Urban Energy Resources to Achieve Net-Zero Energy Neighborhoods
Bibliographic record
Abstract
This paper summarizes a methodology developed to optimize urban-scale energy mix. An optimal capacity estimation method based on energy credits is proposed, the objective of which is to plan renewable and alternative energy sources to yield zero (or positive) year-end energy credits. Several renewable and alternative energy resources are considered, including photovoltaic systems, solar thermal collectors, wind turbines, waste to energy (WtE) potential, as well as thermal seasonal storage. The methodology employs several energy simulation and optimization tools, including Energy Plus, TRNSYS and MATLAB. The optimization employs a non-linear process that uses objective function, boundaries and non-linear/linear constraints as input. The methodology is demonstrated on a hypothetical mixed-use neighborhood, designed to achieve high-energy performance objectives, with three scenarios of energy operations: 1) all electric, 2) all-electric except for DHW, and 3) DHW and space heating arenon-electric. The pilot location of this mixed-use neighborhood, including residential and commercial buildings, is Calgary (AB, Canada). For the all-electric scenario, PV systems implemented in all available south facing roof areas together with a limited number of wind turbines can achieve NZE status. For the other two scenarios, solar thermal collectors coupled to borehole thermal storage (STC and BTES) need to be considered. Although in all cases of the considered scenarios waste-based energy is not required, it can be used to shave the peak electric load, reducing the stress on the grid. This methodology can be employed for the design of an integrated urban energy systems, in different neighborhood designs, to achieve energy self-sufficient, or energy positive status.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".