Optimizing phase change material integration in residential building envelopes for year-round energy efficiency in cold climates
Bibliographic record
Abstract
Phase Change Materials (PCMs) hold significant potential for improving traditional building envelopes by mitigating indoor temperature fluctuations and reducing energy demands through their Thermal Energy Storage (TES) properties. A crucial objective in designing PCM-enhanced building envelopes is to optimize their energy-saving performance under varying conditions. This simulation study focuses on a residential building in Alberta, Canada, analyzing both steady (normal) and intermittent (night) operation schedules. The aim is to identify PCM specifications that maximize year-round energy-saving. The two main variables of PCM specifications investigated are the midpoint melting temperature ( T mid ) and the installation position (PCM layer on the interior or exterior side of the insulation layer). Preliminary simulations show that PCMs installed in the interior outperform those installed in exterior locations. If true, the optimization problem is simplified to a one-dimensional model, with T mid being the continuous variable optimized to minimize cooling, heating, and total energy demand on an annual basis, respectively. The co-simulation of EnergyPlus and GenOpt platforms is employed for optimization. Results indicate that the PCM configuration with the optimal midpoint melting temperature T mid resulted in total energy-saving of 6.65% at 21.71°C for the steady schedule and 5.21% at 22.04°C for the intermittent schedule. And the ratio of energy-saving for cooling was higher under intermittent operation (28.42% at 23.27°C) than under steady operation (22.38% at 22.76°C). Relatively satisfactory heating or cooling energy-saving was achieved when T mid was set within ±0.5°C of the heating setpoint or 1°C–2°C below the cooling setpoint, respectively. For residential buildings in cold climates, the melting heat of PCMs primarily originates from indoor sources rather than outdoors. While the energy savings from PCMs during the winter are modest, their ability to mitigate indoor temperature fluctuations is significantly enhanced under intermittent operations, showing promise in enhancing thermal comfort and improving building energy flexibility.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".