Building-to-vehicle-to-building approach for the NZEB target at a micro-grid level: a comprehensive sensitivity and parametric post-optimality analysis
Bibliographic record
Abstract
Abstract This paper focuses on a novel energy management approach for cluster of buildings connected in micro-grids by taking advantages from plug-in electric vehicles considered as vector devices for renewable energy exchanges, besides additional high-power appliances and house electricity sources. Such approach allows accelerating the development of nearly zero energy buildings and promoting the deployment of renewable energy sources at a micro grid level. To this aim, a dynamic simulation model, implemented in MatLab was developed for the building energy demands and loads assessment. To show the potentiality of the considered concept and approach, a case study based on a micro-grid consisting of a house and an office building connected by an electric vehicle is presented. The optimization of three different layouts, where electricity is alternatively produced by tilted roof or vertical façade photovoltaic panels is conducted by means of a parametric analysis performed by varying the vehicles energy use, battery capacities and solar field size. Preliminary results show that, by considering economic criteria, by exploiting the renewable energy production on and off-site, the buildings final demands decrease to values lower than those commonly considered for NZEBs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".