Optimal Distributed Generation Alocation to Find Optimal Voltage Profile With MinimumDG Investment Cost in a Smart Neighborhood
Bibliographic record
Abstract
When talking about the effect of renewable energy, our thinking can be exemplified by an action of “placing an icing on the cake”. We must have a good building, to which we add renewable energy sources. The authors however, reverse the traditional design process and starting an integrated design process, we ask the question – how can we design an affordable, energy efficient building that the effect of the renewable energy sources is reinforced? We start with a system that must fulfill several technical requirements and one of the synergies in the design process will be to effectively incorporate the renewable energy sources. Changing the paradigm of design is the result of actual construction development in countries like Canada, USA and Japan and while we are looking at this trend from the scientific point of view, we are also be able to illustrate the science behind the next generation of the construction retrofitting with practical examples from these three counties. In effect, this short note becomes a conceptual progress report on energy efficiency in thermal upgrade of buildings. Keywords: energy efficiency; building automatic control; energy use under field conditions; two-stage construction process; cost-benefit evaluation; deep retrofit of residential buildings
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".