Solar energy on the Moon for fixed or tracked photovoltaic systems
Bibliographic record
Abstract
Moon exploration will require local renewable energy production system, which design needs estimations of the received solar energy as a function of the location and the installation mode. We developed a novel method to compute the solar energy received by a 1 m2 flat surface anywhere on the Moon, for any period and using four different installation modes used for photovoltaic systems (fixed, 1-axis tracked vertical or horizontal and 2-axis tracked). By computing hourly elevations and azimuths of the Sun from the lunar year 2012 to 2031, we determined the incident angle between the solar rays and the surface, enabling the calculation of the solar energy received over a 20-year cycle, encompassing nearly all Sun-Moon relative positions on a human scale. We applied this method to compare the solar energy received on a one-axis tracked surface (vertical or horizontal axis), a two-axis tracked surface and a fixed surface at optimal azimuth and tilt, at ten locations from equator to poles. While the two-axis tracker exhibits the highest solar energy levels, comparable solar energies are observed near the poles with a vertical axis tracker and near the equator with a horizontal axis tracker. A fixed system, on the other hand, experiences a significant loss of solar energy in comparison to a two-axis tracker, ranging from 37% to 64%. Additionally, we showed that the partial Sun visibility results in reduced solar energy levels, particularly prevalent near the poles where the Sun remains close to the horizon. Near the poles, a vertical axis tracker seems the best solution, and could be theoretically applied with a perfect concentrator photovoltaic system with an acceptance angle above +/−3.5°.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".