Time averaging and threshold effect on statistics of residential power consumption
Bibliographic record
Abstract
With the help of smart meters, power companies can remotely collect customers' power consumption and setup demand response programs accordingly. While there is no standard for the metering interval, many power companies collect data at hourly basis. Hourly measured data is sufficient for providing billing information and feedback on energy use. However, it does not reflect the true inter-hour dynamics of power and energy usage of residential or commercial consumers. Higher resolution electricity consumption information is important in setting up real-time demand response programs. In order to increase the information value of the collected data, power companies may simply increase the metering resolution or to adapt alternative metering methods. Threshold metering, which acquires data only when the change of measurements exceeds a certain level, is an alternative metering option. This paper compares interval and threshold metering methods with different settings for measuring domestic power consumptions. Instantaneous power consumption data are collected from four different houses in Calgary, Canada and these data are used to generate test data for interval and threshold metering study. Numerical results of statistical and accuracy measures and data size tradeoffs are provided to support our discussion.
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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) | 0.001 | 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 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".