Optimization of phase change material integration in residential building envelopes in cold climates: energy and economic performance analysis
Bibliographic record
Abstract
Phase Change Materials (PCMs) are increasingly explored in building energy research for their ability to reduce indoor temperature fluctuations and lower energy demands through latent heat storage. This thesis investigates PCM integration in residential building envelopes across Canadian cold climates, combining energy performance optimization with economic feasibility assessments. Two projects are presented: the first focused on identifying energy-optimal PCM configurations for a residential building under constant and intermittent operations, while the second expands the investigation into multi-objective optimizations across multiple cities and envelope types, considering techno-economic trade-offs. The first project examines a single-family residential prototype located in Edmonton, Alberta, under two typical internal operation schedules: constant (24-hour conditioned) and intermittent (nighttime-only). A co-simulation platform integrating EnergyPlus and GenOpt is used to optimize the midpoint melting temperature (Tₘ) of interior-installed PCMs for annual heating, cooling, and total energy load minimization. Results show that interior installation significantly outperforms exterior placement due to better thermal coupling with indoor spaces. The optimal Tₘ values were found to be 21.71 °C for steady and 22.04 °C for intermittent operation, achieving total energy savings of 6.65 percent and 5.21 percent, respectively. Cooling energy savings were notably higher under intermittent operation, reaching up to 28.42 percent, reflecting increased PCM activation during unoccupied daytime periods. Daily and hourly simulation analyses further demonstrate the materials' buffering effect in transitional seasons. While winter savings remain modest due to limited phase-change activation, PCM layers substantially enhance indoor thermal stability, supporting improved occupant comfort and energy demand flexibility. These results highlight the importance of aligning Tₘ with thermostat settings, ideally within 0.5 °C of the heating setpoint or 1 °C to 2 °C below the cooling setpoint. The second project develops a techno-economic optimization framework for PCM-integrated multi-family buildings, applied to four representative Canadian cities: Vancouver, Toronto, Montreal, and Edmonton. Thirteen PCM-related design variables, including temperature-enthalpy curves, installation positions, and layer thickness, are optimized using the NSGA-II coupled with EnergyPlus simulations and a custom data-logging mechanism in MATLAB. Results reveal a consistent trade-off between the source energy use and total cost, forming a concave Pareto front. Optimal solutions favor interior PCM placement, narrow melting ranges between 1 °C and 3 °C, and midpoint melting temperatures between 21 °C and 24 °C. A key contribution of this work is the detailed HVAC analysis, particularly regarding cooling performance. PCM-enhanced envelopes reduce peak cooling loads, extend cooling durations, and flatten daily load profiles, promoting thermal load redistribution to off-peak hours. This redistribution supports the use of part-load-efficient HVAC systems and helps alleviate peak period demand, although the overall coefficient of performance (COP) showed minimal increase under the single-speed DX cooling system. Cooling system sizing simulations show that PCM application can reduce design capacities by up to 25 percent. However, economic analysis indicates that most current PCM configurations remain cost-prohibitive with high payback periods, but mild PCM use cases exhibit improved cost-effectiveness. Benefit-cost ratio analysis across cities and envelope types identified the investment priorities for different cities and envelope types, favoring high utility price cities and exterior wall assemblies. Break-even price analysis further indicates that viable PCM unit costs should fall below 1 Canadian dollar per kilogram to enable broader adoption. In conclusion, this thesis presents a comprehensive evaluation of PCM-enhanced residential building envelopes in cold climates. By combining energy and economic optimization, it provides practical insights into how PCM design, building operation patterns, and HVAC interactions influence performance and cost. The findings support future development of energy-flexible, thermally stable, and cost-efficient building design strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".