Representative Time-Series Scenarios of Residential Power Demand, Electric Vehicle Charging, and Solar Photovoltaic Generation Based on Unsupervised Learning Algorithms
Bibliographic record
Abstract
The definition of representative scenarios can be beneficial to the modelling of power distribution networks, reducing the computational burden of optimization models that require data from several power demand and solar photovoltaic (PV) generation profiles. We propose a method that cluster net power demand profiles from households connected at the low-voltage (LV) distribution network. After a comprehensive study of different clustering algorithms, an agglomerative hierarchical clustering algorithm was employed to generate a dendrogram that facilitates the identification of cluster hierarchy. The output of the proposed method includes representative scenarios on net power demand, representing the behavior of customer's appliances, electric vehicles (EVs), and rooftop PV systems. The method also enables acquiring clusters of disaggregated data on gross power demand, EV charging, and solar PV generation profiles. The results show that the agglomerative hierarchical clustering method merged clusters with lower imbalance while k-means and k-means++ clustering methods led to a high imbalance of time-series profiles within clusters. Additionally, the defined centroids did not represent peaks and valleys of power demand and solar PV generation, while medoids kept some of the peaks and valleys from the power profiles observed in the original data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".