Optimization of Solar Photovoltaics Tilt Angle for Hybrid Solar-Geothermal Heat Pump Applications in Extreme Cold Climate Regions of Northwestern Ontario, Canada
Bibliographic record
Abstract
The global imperative to mitigate greenhouse gas emissions, combat air pollution, and manage escalating energy costs and demand underscores the critical role of renewable energy technologies. Solar photovoltaic (PV) systems present a viable solution by directly converting sunlight into electricity, especially for energy-intensive facilities in regions with high solar potential, such as Northwestern Ontario, Canada. The tilt angle of a PV panel is a fundamental parameter that significantly influences the amount of incident solar radiation, thereby directly affecting the system's power output and overall economic efficiency. This study employs location-specific experimental data and validated numerical modeling to determine the optimal monthly and annual tilt angles for a PV array intended to potentially power a geothermal heat pump (GHP) at the Thunder Bay Regional Health Sciences Centre located in a severe cold climate region of Northwestern Ontario. Optimizing this angle enhances efficiency, reduces operating costs, and improves the commercial viability of the hybrid system. Analysis revealed that the monthly optimal tilt angle varies from 3° in June to a maximum of 69° in December and January. The annual fixed-angle optimum is approximately 39°. Solar irradiance peaks in June at roughly 5.14 kWh/m², falling to its annual minimum in December at about 49.2% of this peak. To specifically support the GHP during its peak heating demand, a winter-optimized tilt of 67° is recommended. This configuration captures an average maximum of 3.14 kWh/m²/day during the coldest months, aligning solar energy harvest with the facility's highest heating loads. Furthermore, the site experiences a substantial variation in available solar energy hours, with monthly average daily daylight hours ranging from a low of 8.2 hours (in December) to a high of 15.8 hours (in June). The estimated annual optimum tilt angle of 39° for TBRHSC aligns with the 31°–45° range reported for similar latitudes, and the corresponding predicted solar irradiance of 3.94 kWh/m²/day shows good agreement (<7% difference) with international benchmarks. The study's primary applied contribution is the proposal of a load-specific, winter-optimized tilt of 67°—a strategic adaptation designed to enhance the technical and economic feasibility of integrated solar-GHP systems operating under the harsh climatic constraints of Northwestern Ontario.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".