Evaluation of Dimensionality Reduction Techniques for Load Profiling Application in Smart Grid Environment
Bibliographic record
Abstract
The traditional power grid system is evolving toward a smart grid system, which will improve the user experience. However, this system is capable of generating high-dimensional data at a very high sample rate. A common technique for reducing high-dimensional data is dimensionality reduction. With this technique, we are able to reduce the data to a lower dimension, making it suitable for smart grid applications including transmission, storage, and visualization. Linear dimensionality reduction techniques are mostly explored in the context of smart grid applications. Due to the nonlinear nature of data generation in the smart grid, we anticipate that the nonlinear dimensionality reduction techniques can perform better. This work evaluates different nonlinear dimensionality reduction techniques and compares them with principal component analysis, which is a widely used linear dimensionality reduction technique in the smart grid environment. We use the visualization of load profile data and adjusted rank index (ARI) for comparison of dimensionality reduction techniques. Load profiling is an important task to complement the demand-side management and tariff selection. The visual depiction of the load profiles and ARI suggest that the nonlinear dimensionality reduction techniques perform better compared with linear dimensionality reduction techniques.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".