Performance analysis of dimensionality reduction techniques for demand side management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The advancement of the electric grid has led to tremendous growth in data generated from the installed sensors. Smart meters measure the electric energy usage of a consumer, transmit the measured data to the utility and receive pricing information. This requires a two way communication between the utility and the end user. With the projected increase in the number of deployed smart meters, utilities would be facing challenges in handling huge quantities of data, referred to as Big Data. For the analysis of the large data to be tractable, we need to extract important lower dimensional features from raw measurements. In this paper we critically analyze dimensionality reduction of smart meter data for smart grid applications. We compare performance of two dimensionality reduction techniques, Random Projection and Principal Component Analysis, on projecting smart meters data onto a linear subspace of reduced dimensions. We compute the Euclidean distance between pair of data samples in the original and reduced dimensions and obtained the mean and standard deviation of the relative error. Additionally, we cluster the users using the original data and after applying dimensionality reduction. The sum of square error (SSE), distance between datapoints and the centroid in a given cluster, is used to compare the clustering performance of the two techniques.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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 it