Photovoltaic Power and Meteorological Datasets With Snow Detection From the Outdoor Solar Power Laboratories of the Finnish Meteorological Institute
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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