The Potential of PV Module Tilt and Technology for Tuning Daily Energy Yield across the Year
Why this work is in the frame
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Bibliographic record
Abstract
In the future, with costs for PV modules rapidly declining and an expected increase of PV penetration in energy production, the cost for PV installations may be impacted more and more by balancing costs of storage, load management, and curtailment, rather than the currently dominating hardware components (PV modules and BOS components) and their installation. While the impact of the temperature coefficient has been investigated and published, in this paper we want to also include the impact of tilt. With our physics-based bottom-up modeling approach, taking into account coupled optical, thermal and electrical effects we quantify with high accuracy PV production throughout the year. The modeling allows a very fine time granularity, but here in particular, we want to look at daily and seasonal energy yield (rather than intra-day generation). Additionally, we want to focus more on the locations with a high potential mismatch between PV generation (with a traditional system optimized for yearly energy yield) and electricity/energy consumption throughout the year. As such, Ottawa is chosen for its high potential with high relative irradiance and very low temperatures in winter. With its climate data, energy yield then simulated as a function of the tilt of the module, as well as its technology, in the form of its temperature coefficient. The parameters of the modules are extracted from commercial datasheets in order to be as relevant as possible. For a tilt optimized towards yearly energy yield, a module with a high temperature coefficient can result already in an improved energy yield over the winter season of 1% compared to a module with a low temperature coefficient, and an additional 1% in winter and in fall if also the low-light behaviour is increased for the high temperature coefficient module. If the tilt is also considered for optimization, we indicate how further improvements may be possible, though simulations towards this end are still being streamlined and results taking into account both effects of temperature coefficient and tilt will be produced in the coming months.
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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