Representative Profiling of Prosumers with Local Distributed Energy Resources and Electric Vehicles Using Unsupervised Machine Learning
Bibliographic record
Abstract
In this paper, the representative profiles of residential prosumers owning local distributed energy resources (L-DERs) and plug-in electric vehicles (PEV) at different levels of generation and demand are identified. The Pecan Street household dataset is used in this work with high-granularity data of one second and it includes the roof-top solar photovoltaic (PV), home battery energy storage system (HBESS) and PEV profiles. Because of the large variance in the data due to such high granularity and different levels of generation/demand in the residential profiles, this study presents a systematic approach to identify a comprehensive list of representative profiles. The machine learning techniques such as principal component analysis (PCA), and the unsupervised K-means clustering and K-nearest neighbor are employed to identify the representative profiles. The study included 123 residential homes, which includes a set of different combinations of PVs, PEVs and HBESS and the results have shown that they can be represented by only 17 representative profiles. This reduction in the number of representative profiles at such high-granularity will lead to significant advances in accelerating the distribution system time-series analysis studies in particular when considering the presence of prosumers with LDERs and PEVs.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".