Energy Savings from Retrofitting Residential Buildings with Phase Change Material Integrated Panel
Bibliographic record
Abstract
The building sector in Canada is energy intensive and a significant portion of the current residential building stock is decades old with their envelope requiring upgrades to meet current energy efficiency standards. This thesis investigates the energy savings from incorporating thermal energy storage using phase change materials (PCM) in retrofitting existing building stock. A building recently retrofitted in Ottawa is used as a reference for this thesis. In this study, a honeycomb PCM wallboard with a latent heat capacity of 42.7 kJ/kg and high thermal conductivity of 2.7 W/mK was selected and we performed an extensive numerical investigation on the incorporation of the PCM to the retrofit panel attached to the reference building walls. In the third chapter, we performed a parametric study to understand the effect of the phase change material parameters in reducing the heat flux through the building envelope using representative summer and winter days. The parameters considered were the melting temperature of the PCM, the thickness of the PCM and the peak equivalent heat capacity/ latent heat capacity and we analyzed how these parameters affected heat flux reduction through the building walls. It was determined that the optimal PCM melting temperature was closely related to the indoor setpoint. In the fourth chapter, we determined the effect of the positioning of the PCM and the climatic condition on the effectiveness of the PCM by performing annual simulations. The optimal energy savings were obtained when the PCM was placed on the interior side of the retrofit panel, and we observed a reduction in energy savings as the PCM was moved toward the exterior side of the retrofit panel. The annual simulation was performed for the tropical climate of Brasilia, Brazil to show the effect of climatic conditions. We noted that for year-round utilization of the latent heat storage of the PCM, the diurnal temperature swings need to overlap with the indoor temperature. Our results showed the PCM to be more effective between May and September in Ottawa and we realized heating and cooling savings of 13% and 8% respectively in these months. Economic analysis was performed to determine the payback period. Our conservative estimate based on the residential electricity price of CA$ 0.17 shows that the investment could be realized in 15.6 years. The current residential electricity price in Europe was used to estimate the payback period to get a global view and the result showed the energy savings from PCM integration can return the initial investment in 7.6 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".