A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites
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
Roof-top solar photovoltaic systems are normally invisible to system operators, meaning that their generated power is not monitored. If a significant number of systems are installed, invisible solar power could significantly alter the net load in power systems. In this paper, a data-driven methodology is proposed to estimate the power generation of invisible solar power sites by using the measured values from a small number of representative sites. The proposed methodology is composed of a data dimension reduction engine and a mapping function. A number of established methods for reducing the dimension of large-scale data is investigated, and a hybrid method based on k -means clustering and principal component analysis is proposed. The output of this block provides a small subset of sites whose measured data are used in the mapping function. We have implemented several mapping functions to estimate the total generation power of all sites based on the measured output of the selected subset of sites. Numerical results based on data from California's power system are presented.
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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 | 0.000 | 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) | 0.000 | 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