Assessing the Potential of Phase-Change Materials in Energy Retrofitting of Existing Buildings in a Mediterranean Climate
Bibliographic record
Abstract
The European Community has prioritized reducing energy consumption and improving energy efficiency in the building sector, along with ensuring increasingly high standards of thermal comfort, as key goals over recent decades. Given the impact of climate change, the rising frequency of extreme weather events, and the rapid shifts in peak demand during both winter and summer, buildings must efficiently respond to sudden and extreme temperature fluctuations while maintaining optimal indoor comfort. Phase-change materials (PCMs), which can adapt their thermophysical properties in response to external conditions, may offer a solution for enhancing building resilience to climate change. This paper evaluates the benefits of integrating various PCMs with plasterboard in the energy retrofit of a multi-family complex in a Mediterranean climate. The study examines the application of a PCM with a melting temperature of 25 °C at three different thicknesses (74.2 mm, 37.1 mm, and 20.8 mm) to external walls, ceilings, and both walls and ceilings simultaneously. Among the various applications, using the PCM on walls alone maximized heating savings as thickness increased (26.6%), while ceiling application maximized cooling energy savings (17.5%). Combined solutions offered the most balanced seasonal benefits, leading to the greatest overall energy reductions (24.1%).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".