Photovoltaic Power and Meteorological Datasets With Snow Detection From the Outdoor Solar Power Laboratories of the Finnish Meteorological Institute
Bibliographic record
Abstract
ABSTRACT High‐quality, long‐term time series of photovoltaic (PV) output measurements are scarce at high latitudes, limiting both academic research and commercial applications. Here, we describe and publish high‐resolution (1 min) PV output data—together with ancillary measurements—from three high‐latitude sites in Finland covering 26 August 2015 to 31 December 2021. The PV data, comprising averaged power readings, were retrieved from inverter registries. Ancillary measurements from the PV field—plane‐of‐array irradiance, air temperature, module temperature, and photographs of the modules—were collected using dedicated instrumentation. Additional meteorological variables, including solar radiation components and snow depth, were obtained from nearby Finnish Meteorological Institute (FMI) weather stations. Daily snow cover classification of the modules was performed manually from daily plots of PV, ancillary and meteorological data and partially validated with photographs. Beyond visual inspection, the PV data underwent the quality control routine as described in a recent paper by Visser and colleagues; however, we found the routine exhibits several shortcomings under high latitude conditions. Snow coverage on the PV modules varied significantly with site location and system design. Subsets of the dataset have previously been used for PV output‐model validation. The complete dataset offers further opportunities, including PV model development, refinement of performance metrics and quality control methods for high‐latitude installations, and investigations of snow‐related losses and gains. The data is freely available from the FMI METIS data repository.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".