Modeling and Design of Building-Integrated Photovoltaic/Thermal Systems with Embedded Thermal Storage
Bibliographic record
Abstract
Hamidreza Zarrinkafsh, MSc. Concordia University 2025 This thesis presents a novel prefabricated modular building-integrated photovoltaic/thermal (BIPV/T) system design that integrates semi-transparent bi-facial photovoltaic (STPV) panels and thermal energy storage (TES) using ultra-high-performance concrete (UHPC) to enhance electrical, thermal, and architectural performance. It investigates the development, modeling, and experimental validation of this system to address the need for scalable, efficient, and resilient renewable energy solutions for building envelopes. A comprehensive literature review on various BIPV/T systems showed the necessity of enhanced air-based configurations that fulfill both mechanical and aesthetic requirements. In response, this research introduces three main innovations together: a prefabricated modular curtain wall concept, integration of STPV panels, and most importantly, the use of UHPC as an embedded thermal storage material. Among these, UHPC provides the greatest advancement by reducing outlet air temperature fluctuations, and making the building envelope more durable, and fire-resistant. A two-dimensional finite-difference numerical model was developed to simulate thermal behavior and predict system performance. This model was validated through experiments on a full-scale system tested in a controlled environmental chamber under extreme cold conditions (−15 °C and −25 °C) using a solar simulator with 800 W/m² irradiance. The system, designed with a curtain wall mounting system and frameless STPV panels. It was equipped with thermocouples, RTDs, and a data acquisition system to monitor temperature distribution. Uniformity and infrared imaging tests were conducted to confirm measurement reliability. The experimental results showed thermal efficiencies of 37% and 50%, with outlet air temperatures exceeding ambient conditions by over 20 °C, demonstrating effective heat storage of the UHPC thermal storage. The developed model predictions matched the experimental data, validating the simulation with a correlation coefficient of R² = 0.92. The validated model was used to perform sensitivity analyses on various parameters, including airflow rates within the cavity (0.6 and 1.0 m/s), cavity depths (3 and 4 cm), solar irradiance (200 and 800 W/m2), and system height (2 and 6 m), as well as the thermal conductivity of the concrete (1, 2, and 3 W/m·K) and its thickness (1 to 2.5 cm). The system performance was also assessed for a two-story façade configuration to predict the adequacy of the produced heat for integration with HP or domestic hot water applications under realistic conditions on one of the coldest days of the year in Montreal, Canada. Comparison of the system with and without UHPC panel on a two-story façade using real cold-climate data showed a 22.7% reduction in outlet temperature fluctuations and a ~5-hour delay in thermal response, confirming UHPC’s effectiveness as TES in cold climates. Since the system consistently maintained a temperature difference exceeding 20 °C even during the coldest week of the year, its temperature gain significantly reduces the heating load on HVAC systems and improves their operational efficiency. As a result, the system can contribute to peak load shaving and shifting, grid stability and reliability, and lowering overall building energy consumption and bills. Additionally, the passive preheating capability enhances indoor thermal comfort and supports resilient building operation during periods of extreme cold or power disruptions. This thesis makes several key contributions: (1) it introduces ultra-high-performance concrete (UHPC) as a novel thermal storage material in BIPV/T systems, an area previously underexplored in the literature; (2) it presents a validated experimental and numerical framework for evaluating thermal and electrical performance under real-world conditions; and (3) it demonstrates a modular, prefabricated STPV system compatible with existing curtain wall techniques. These contributions offer a scalable pathway for improving energy efficiency, occupant comfort, and renewable energy adoption in high-performance building design. In conclusion, this thesis presents a novel BIPV/T system with a validated model for the design of enhanced air-based BIPV/T systems integrating UHPC thermal storage, modular curtain wall assembly, and semi-transparent PV modules.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".