Performance Analysis of a Photovoltaic/Thermal (PV/T) System in a Cold Climate
Bibliographic record
Abstract
This thesis investigates the technical, economic, and environmental performance of a photovoltaic/thermal (PV/T) system for the Administration Building at the University of Alberta in Edmonton. Using Python-based simulations and DesignBuilder modeling, the study evaluates the operation of 80 roof mounted PV/T panels in a cold climate. Two configurations are assessed for integration with the PV/T system to determine which offers greater overall value. The system demonstrated strong technical performance, producing significant amounts of electricity and thermal energy annually, with notable electrical and thermal efficiencies. Compared to a conventional PV system, the PV/T setup yielded higher electrical output while simultaneously supplying usable heat. Two integration scenarios were considered to assess the feasibility of PV/T adoption: (i) coupling with the AHP during winter to reduce electricity demand for space heating, and (ii) DHW preheating in summer to favorable inlet temperatures. Together, these scenarios highlight the system’s adaptability to seasonal thermal loads. Economically, the system’s capital cost was estimated, with levelized costs varying by lifespan and payback periods remaining long, though sensitivity analysis showed improved feasibility under favorable market or policy conditions. Environmentally, the system avoided significant CO₂ emissions through clean electricity, fossil fuel displacement, and AHP efficiency gains. Overall, PV/T–AHP integration provides strong heating efficiency and carbon reduction potential, but its economic viability depends on future energy prices and financing.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".