Regression-Based Wintertime Energy Consumption Prediction for Cold Load Pick-Up Management
Bibliographic record
Abstract
Thermostatically Controlled Loads (TCL) have a significant impact on Cold Load Pick-up (CLPU) during distribution system service restoration. Widespread deployment of smart meter devices opens up new opportunities for data-driven load modelling. In this paper, we compare several linear regression approaches to robust short term prediction of hourly energy consumption as a function of the outdoor temperature during the low-temperature season. The goal is to estimate the energy that will not be delivered during an outage for the purpose of a further estimation of the CLPU peak and duration for consumers with TCLs. The prediction is based on smart meter load data and outdoor temperature data. The performance of the proposed regression approaches is analyzed for 25 residential homes from real measured data. Prediction is performed on an hourly basis. The quality of the regression results is compared with the Naïve forecast benchmark method. The results show that autoregression approach outperforms the other methods, however, since this approach is highly depended on the existence of the sequence of the previous load measurements, as an alternative approach, ENS prediction is successfully performed using only temperature data.
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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) | 0.000 | 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 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".