Efficient Multijunction Solar Cell Design for Maximum Annual Energy Yield by Representative Spectrum Selection
Why this work is in the frame
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Bibliographic record
Abstract
We describe a systematic approach to multijunction solar cell (MJSC) design that unambiguously identifies the spectrum to be used in cell optimization such that local annual energy yield is maximized. A set of candidate spectra is generated from air mass (AM) values ranging from AM1d to AM5d. Each candidate spectrum is used to find the bandgap combination that maximizes cell efficiency and its energy yield is then calculated using an efficient data reduction approach. The bandgap combination that maximizes annual energy yield identifies the representative spectrum. We do this for cells with up to eight junctions across all clear-sky latitudes and compare our results to other cell optimization approaches. Our representative spectrum selection (RSS) approach is robust and highly tolerant of variations in latitude, particularly when compared to the standard AM1.5d approach which, at midlatitudes, cannot be used without suffering an increasingly severe yield penalty. Comparison against the 50% cumulative energy AM (50% AM) design approach is enabled by using the same design conditions (sea level and ASTM standard atmosphere) in order to unambiguously associate each 50% AM value with a cell design spectrum. We find that our RSS approach always produces cells with slightly higher annual energy yields than are achieved by the corresponding 50% AM designs. While both approaches show similar yields for devices with few junctions, we find yield enhancements approaching 1% for cell designs with many junctions, emphasizing the need to consider the spectral variability of the local solar resource. This consideration is systematically enabled by our RSS approach, addressing a deficiency in the previous design approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it