“En-Solex”: A Novel Solar Exoskeleton for the Energy-efficiency Retrofitting of Existing Buildings
Bibliographic record
Abstract
The energy retrofitting of the existing building stock is one of the current challenging strategic objectives on the way to the European target of climate neutrality by 2050. According to the Renovation Wave plan, around 35 million existing buildings need to be upgraded to the highest energy efficiency level by 2030, and innovative technological solutions are required to achieve this ambitious goal. This paper proposes a novel solar exoskeleton for the energy and architectural retrofitting of existing buildings, called En-Solex. The system, which consists of an external steel frame that wraps around buildings like a double skin, combines passive solar gain control (shading and greening) with high-efficiency active solar systems (PV panels) optimised for integration into existing building facades. The energy-saving potential of the system with different façade configurations is evaluated on a multi-family residential building located in a Mediterranean climate. The dynamic energy simulations show that the proposed solution can reduce the energy demand for space heating and cooling by 33.4% and 25.5% respectively. The En-Solex system integration combined with generator replacement results in a maximum heating and cooling reduction equal to 80.7% and 59.6% respectively. The surplus of electricity generated, thanks to the integration of RES, can lead to a net plus target, with the building exceeding its average annual electricity demand.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".