Performance assessment of a 20 MW photovoltaic power plant in a hot climate using real data and simulation tools
Bibliographic record
Abstract
The present study aims to evaluate the aptness of two commercial simulators, HOMER Pro and RETScreen Expert, as predictors of the performance of a large-scale photovoltaic power plant designed to deliver up to 20 MW in a hot climate, for which 26 months of real operational data are available. The power plant is located in the province of Adrar in the south of Algeria and classified as one of the hot regions worldwide. Performance parameters were reference yield, performance ratio, capacity factor, temperature loss and statistical indicators. The results showed that photovoltaic power plant performance depends on cell technology, insolation, and environmental conditions, especially temperature. The deviations between the simulation results and real monitoring data were found to be smaller in the case of HOMER Pro simulation tool. The total annual energy supplied in 2018 by the power plant was 36364MWh, whereas RETScreen Expert predicted 42339 MWh, or about 14% more and HOMER Pro predicted 34508 MWh or about 5.1% less. The influence of temperature on the power plant output was strong, causing a 40% drop during the summer, due to the limitations of the polycrystalline cell technology. This needs to be considered in the design of future photovoltaic power plants to be operated in hot climates. HOMER Pro and RETScreen Expert predicted an average annual final yield of 5.128 h/day, a module efficiency of 15% and an inverter efficiency of 98%. The t statistics were 3.75 for HOMER Pro and 6.12 for RETScreen Expert. The analysis shows that the 20 MW photovoltaic plant in hot climate experiences high losses compared to an equivalent plant based on thin-film photovoltaic cells.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".