Tuning electricity generation throughout the year with PV module technology
Bibliographic record
Abstract
Currently, photovoltaic (PV) installations target a maximization of annual energy yield. In the future however, electricity generation may need to match better with the load profiles in a given environment and climate. In particular this will be a challenge for generation across the seasons, where electrical storage is less suitable, and in the built environment, where wind turbines for generation are much more difficult to integrate. In this paper we discuss how this challenge may be addressed with climate- and consumption-specific PV module technology. In particular, we demonstrate how the temperature coefficient of a PV system can impact the energy yield throughout the year. After explaining the concept, we apply our electrical-optical-thermal model to do very accurate physics-based bottom-up simulations in different climates. As such, depending on the climate and latitude, a higher temperature coefficient of the PV module may lead to higher energy yields, mostly during the colder season. We also demonstrate that, if higher temperature coefficients are accompanied by improved low-light performance (tunable using the module’s series resistance), the seasonal gain can be much higher. We indicate the relevance of our assumptions by basing the module performance in the simulations on (datasheets of) commercial modules.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".