Computing Electricity Consumption Profiles from Household Smart Meter Data
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate a critical problem in smart meter data mining: computing electricity consumption profiles. We present a simple, interpretable and practical profiling framework for residential consumers, which accounts for variations in electricity consumption at di↵erent times of day and at di↵erent external temperatures. Our approach is to isolate the e↵ect of external temperature on electricity consumption and apply a time-series autoregressive model to the remaining signal. The proposed profiles may be used for making personalized energy-saving recommendations, detecting outliers, and generating very large realistic data sets for testing the scalability of smart meter data management systems. Using predictive power as a metric for the accuracy of consumption profiles, we show, using a real data set of 1000 homes, that our approach results in improved root-mean-squared prediction error compared to existing approaches. 1.
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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 | 0.000 | 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 it