An Analytical Method for Probabilistic Modeling of the Steady-State Behavior of Secondary Residential System
Bibliographic record
Abstract
Effective modeling of secondary residential system is critical for numerous demand side innovations in smart grid, such as distributed generation and energy storage integration, residential micro-grid operation, and demand response programs. Traditionally, Monte Carlo simulation (MCS) method is used to establish the model by capturing the random usage patterns of home appliances. Yet, the computational effort is tremendous, especially for a large group of residential loads. To address this issue, an analytical method is proposed in this paper. The result is a time varying probabilistic steady-state equivalent circuit model whose parameters are characterized by means and covariance matrices. The proposed method consists of three main components with regard to the derivation of state probability distributions for individual appliances, modeling of aggregated secondary feeder with all appliances, and unscented transformation to combine the aggregated secondary feeder model and service transformer model. The effectiveness and efficiency of the proposed method are evaluated based on extensive numerical results and by comparing the results with that obtained by MCS method.
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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 | 0.000 |
| 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 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".