How Much Building Renewable Energy Is Enough? The Vertical City Weather Generator (VCWG v1.4.4)
Bibliographic record
Abstract
A challenge in the integration of renewable and alternative energy systems for buildings is the determination of the renewable energy ratio, which involves the selection and sizing of appropriate building systems. To address this need, a micro climate-weather software titled the Vertical City Weather Generator (VCWG) is further developed to include renewable and alternative energy systems and account for full two-way interaction between the building system and outdoor environment. VCWG is forced to simulate performance of a residential building in Guelph, Canada, for an entire year in 2015. Various energy options are considered and further optimized for the building to reduce natural gas consumption, electricity consumption, and cost. On an annual basis using the global cost method, and compared to a building with no such renewable or alternative energy systems, the optimized system resulted in 80.3% savings in natural gas consumption, 73.4% savings in electricity consumption, and 3% savings is annualized cost. According to this analysis, some technologies, such as photovoltaics are more favorable in the Canadian climate than other technologies. It is suggested that the building optimization process is not unique, and it depends on background climate, optimization weighing factors, and assumptions used in the economic analysis, which require further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".