A Smart and Safe Electricity Consumption Model for Integrated Energy System Based on Electric Big Data
Bibliographic record
Abstract
In the integrated energy system, the smart and safe electricity consumption requires complex computation and faces high safety risk. To solve the problem, this paper designs a smart and safe electricity consumption model for integrated energy system based on electric big data. Firstly, an aggregate return index was designed based on clustering degree and dispersion degree to automatically optimize the number of classes, and facilitate the k-means clustering (KMC). Next, the optimization criterion for the behavior features of smart and safe electricity consumption was proposed, in which the effectiveness and correlations of the features are measured by the amount of mutual information and the degree of correlation, respectively. After that, the authors put forward a feature optimization strategy for smart and safe electricity consumption behaviors. By this strategy, effective and independent features were selected to form a simplified feature set for the clustering of smart and safe electricity consumption behaviors. On this basis, a smart and safe electricity consumption model was presented for integrated energy system. The effectiveness of our model was confirmed through example analysis.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".