Scalable Local Short-Term Energy Consumption Forecasting
Bibliographic record
Abstract
Smart meter adoption rates are rising across the world and this has contributed to a rapid increase in the type and volume of data being generated. These recent advances have created new opportunities for smart grid research. As energy grids move towards smart grids and specifically towards microgrids, energy demand forecasting must be performed at the local level in order to achieve supply and demand balancing. However, unlike system-level forecasting, short term energy demand forecasting at the local level needs to be highly scalable, because this procedure needs to be completed for potentially hundreds of thousands of customers and within a limited time. This scalability requirement is magnified if the local-level forecasting is to be performed centrally as that is where system-level forecasting is currently performed. To address these challenges, we conducted a systematic study of the scalability and performance of time series forecasting techniques on smart meter data for local level short-term energy consumption. We implemented parallel versions of standard and online forecasting algorithms and evaluated scalability of these algorithms in various settings.
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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".