Energy and Thermal Performance Analysis of PCM-Incorporated Glazing Units Combined with Passive and Active Techniques: A Review Study
Bibliographic record
Abstract
The building envelope provides thermal comfort, an excellent visual view, and sunlight for the occupants. It consists of two parts: (i) an opaque (non-transparent) part (e.g., walls and roofs) and (ii) a transparent part (e.g., windows, curtain walls, and skylight devices). Recently, the use of fully-glazed facades, especially in large cities, has increased due to their aesthetical and structural advantages. This has led this study to review the performance of the currently passive smart glazing technologies. Phase Change Materials (PCMs) as latent energy storage material is the focus of this review, as well as other individual and combined techniques, including shading systems, solar cells (photovoltaic), and chromogenic (thermotropic and thermochromic) materials. PCM-integrated glazing systems have been extensively studied and rapidly developed over the past several decades from the standpoint of unique system designs, such as passive, active, and passive/active mixed designs, intelligent management, and sophisticated controls. In the academic literature, numerous studies on PCM-integrated building envelopes have been conducted, but a comprehensive review of PCM-integrated GUs combined with other passive and active techniques using dialectical analysis and comparing the climatic conditions of each study using Köppen-Geiger climate classification climate classification has been performed only rarely. Consequently, the primary objective of this study is to reduce this discrepancy for all types of glazing, excluding glazed roofs. This review article also contains literature tables as well as highlights, limitations, and further research suggestions at the end of each subsection.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".