First year performance of a 20 MWac PV power plant
Bibliographic record
Abstract
Summary form only given. After one year of continuous operation in Ontario, Canada, the actual performance of the Sarnia 20MWac power plant can be compared to both the long-term energy prediction and the expected energy for the operating year 2010. The long-term prediction uses satellite meteorological data and loss assumptions and the PVSYST system simulation tool to estimate the behavior of the power plant over a "typical" year. Typical meteorological input from satellite data is discretized on a monthly basis; therefore the monthly Performance Ratio (PR) is an appropriate metric for comparison. Our comparison shows that the prediction data is in line with the actual power plant PR during normal operation. However, energy lost due to snowfall remains one major prediction difficulty. Based on the first year's data at the Enbridge's Sarnia 20 MWacpower plant, the power plant is operating within 2.1% of the long-term prediction. Using the one year of on-site meteorological data, the expected energy of the site for 2010 can be found by rerunning the prediction with hourly measured data. At this time-step, specific measurement points within the power plant can be assessed by simply comparing measured to expected values, including the DC and AC energy at the inverters as well as the module temperature and inverter efficiency. The estimation of the modeling error at each of these measurement points shows that there is no single driver of the overall modeling error during normal operation, but also shows that improvements are possible. Actual energy from the Sarnia 20 MWacpower plant was 0.6% more than expected based on the expected energy for the 2010 operating year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.008 | 0.003 |
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".