Global floating PV status and potential
Bibliographic record
Abstract
Abstract Floating photovoltaics (FPV) has been a rapidly growing source of renewable electricity for the past 15 years since first commercial systems were installed. In this work, the insights from SERIS FPV database are shared. This is likely the largest database of its kind and contains the information from 1142 FPV systems in operation, totalling 5.9 GWp, by the end of 2022. Mainland China has been leading FPV installation capacities since 2017 and comprises almost half of the cumulative installed capacity. Similar to land-based PV, FPV installation size has been increasing; the median size has grown from 0.09 MWp in 2013 to 1.40 MWp in 2022, while the median power density has increased from 82 Wp m−2 to 123 Wp m−2 in the same timeframe. The installation cost has fallen simultaneously – the lowest reported was 0.41 USD/Wp in India in 2021. Other pertinent insights from SERIS FPV database include float supplier market share, reported electricity price, water body types and characteristics, as well as its coverage ratio. Finally, the global FPV potential (capacity, energy production, and water saving), for different tilt angles, tracking configurations, and solar panel types are explored. By installing FPV on 10% of the area of 249 717 inland reservoirs, FPV capacity could reach up to 22 TWp and could fulfil the whole global electricity consumption and up to 5% of the world’s water demand. The use of trackers and bifacial panels are advantageous for energy generation in all locations, with trackers increasing specific energy yield of a typical fixed 10° tilt FPV by up to 50% for reservoirs within ± 40 ∘ latitude, while the bifacial gains reach up to 4.5% for all analysed configurations within ± 40 ∘ latitude. The insights from this global FPV market and potential analysis can serve as a reliable reference for FPV stakeholders, researchers, and regulators alike.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".