Open-Source Development of Solar Photovoltaic Technology
Bibliographic record
Abstract
The rise of solar photovoltaic (PV) technology as a driver of rural electrification in the developing world and a contributor to climate change mitigation suggests that innovations enhancing PV efficiency and scalability could make considerable strides in reducing both poverty and greenhouse gas emissions. The nearly global access to the solar resource coupled to innovation-driven decreases in the costs of PV provides a path for a renewable energy source to accelerate sustainable development. Open-source software development has proven to produce reliable and innovative computer code at lower costs than proprietary software through sharing development responsibility with a large community of invested individuals. Concepts of open-source design have been applied to other fields in an attempt to reap the same benefits realized within software development; however, applying open-source strategies to solar PV research is uncommon. This paper reviews and examines how open-source design can be utilized to catalyze rapid innovation in the PV industry. The results show how successful open design and development methods can be created and utilized by identifying business models that provide PV researchers, turnkey suppliers and solar PV module manufacturers with the opportunity to utilize open-source design principles to accelerate innovation. © 2012 International Energy Initiative.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".