Development of integrated performance indicators and integrated energy systems for smart cities
Bibliographic record
Abstract
The urge to develop and innovate net zero energy systems for smart city applications has never been more pressing. As the world population is rapidly growing, the global energy demand expands exponentially. The setback with higher energy consumption is the substantial greenhouse gas emissions associated with using fossil-based fuels, which make up the primary energy sources in the world today. This thesis has two main aspects: it introduces a novel and comprehensive smart city concept composed of 8 domains and 32 indicators, which are computed to make the Smart City Index. On the other hand, it introduces four innovative and integrated net zero energy systems for smart city applications. While simple integration of information communication technology (ICT) applications is integral in the development of smart cities, the concept is much more comprehensive to include smart environment, economy, society, governance, infrastructure, transportation, energy, and pandemic resiliency. In this thesis, 20 cities have been analyzed using four distinct weighing scenarios. Based on the sustainability triad scheme, the city with the highest SCI is Toronto at 0.77, whereas the city with the lowest SCI is Abuja at 0.31. Toronto, Vancouver, and Montreal remain part of the top 5 cities in the equal weighting, sustainability triad and energy focused schemes. In fact, the energy focused scheme places four Canadian cities at the highest SCI, with Montreal at the top, scoring 0.7 and Oshawa at 0.66. Life cycle assessment results show that systems 2 and 3 have higher environmental impacts due to their electricity generation through the Organic Rankine Cycle. Human toxicity is the impact category most affected, followed by global warming and acidification. Furthermore, system optimization is completed in order to reach to the optimal design parameters and model selection. System 4 has the highest energy and exergy efficiencies of 74.6% and 62.9% respectively. This system also accounts for the lowest exergy destruction of only 12% of the total systems destruction. Genetic algorithm optimization results reached a plateau after the 20th generation, with the environment, transportation and pandemic categories having the highest scores.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".