Extraction of Representative Scenarios for Photovoltaic Power With Shared Weight Graph Clustering
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
With the growing integration of photovoltaic (PV) generation, the operational conditions of power systems become more complex and variable. These intricate scenarios place significant pressure on the optimization calculations for power systems, necessitating the extraction of representative scenarios for PV power generation to enhance optimization efficiency. To address this issue, we have proposed a novel clustering model that extracts representative PV output scenarios through the fusion of adaptive feature weights and adjacent density weights. We propose an alternating optimization solution algorithm based on the Lagrange multiplier method and eigenvalue decomposition. The highlight of this work is the dual verification through theoretical proof and simulation experiment. In terms of theoretical proof, we analyze the sensitivity of clustering model parameters, demonstrate algorithm complexity, and theoretically prove the convergence of the proposed solution algorithm. Using actual PV output data from Australia, we validate the high cohesion, low coupling, noise resistance, and parameter sensitivity of the proposed clustering model, as well as the convergence of the proposed solution algorithm. The effectiveness of the proposed method in extracting representative scenarios of PV output has been confirmed through probabilistic power flow analysis using two IEEE test cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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