Novel seasonal application of low-level solar concentration to contribute to net-zero buildings
Bibliographic record
Abstract
Due to excessive fossil fuels consumption, the concentration of carbon dioxide is predicted to be approximately 500 ppm by 2050, impacting global biodiversity for generations. Increasing the percentage of recent solar energy use—sun, wind, hydro, and biomass—to address our global energy needs is of fundamental importance. With buildings representing nearly 38% of global emissions, solar energy can contribute to reduce impact: increasing the productive use of solar insolation from building grounds using reflective tracking mirrors, referred to as Sunflower, is a critical component. To this effect, a Sunflower model is developed from first principles to evaluate the productive use of low-level solar magnification from building grounds and rooftops. This model predicts the solar insolation incident onto a user chosen target with many Sunflowers to contribute to net-zero buildings. The model inputs are flexible, for example, change in number of Sunflower mirrors and their target specifications, including seasonal relocation to optimally reduce energy demand in a building, are part of user inputs. The model utilizes a single ray-tracing method to evaluate the solar irradiation redirected onto the building using low-cost solar tracking mirrors. This model developed in Python is based on solar angles and weather data to calculate the redirected hourly solar flux onto a chosen target. Using NREL’s Solartrace program based on the Monti-Carlo method, model results are validated within an error of 2.35%. The Sunflower model is then applied to predict the displaced energy in an outdoor pool heating application. The pool heating approach using Sunflower bypasses second law inefficiencies as the pool is heated by the irradiations directly from sun without the use of an intermediate thermal fluid. Multiple case scenarios are established to evaluate the optimal mirror angles to increase the solar intensity for a given seasonal configuration. The Sunflower model predictions for applications in Winnipeg, Manitoba (Latitude 49.9o) using 10 Sunflowers shows that seasonal pool heating load can be reduced by 67%, with a yearly GHG savings of 5.1 tons of CO2eq. For this case, the average yearly solar intensity ratio for a single Sunflower with a horizontal target is 1.81, and averages 1.50 during summer months when solar insolation is higher. For a similar application in the remote community of Arviat, Nunavut (Latitude 61.1o), currently depending on diesel fuel, pool water heating requirements can be reduced by 40% using 15 Sunflowers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".