Development of forecasting algorithm for a regional load pattern heavily influenced by a cryptocurrency mining operation
Bibliographic record
Abstract
Many areas in Canada especially Alberta have recently seen big cryptocurrency mining loads appear on their grid. This is an increasing trend. These loads seem to have very unregulated patterns; hence, they tend to make net load forecasts unreliable for small regional load entities like small cities. According to the literature review that was performed, no work was available that looked at forecasting regional load patterns that are heavily influenced by cryptocurrency mining operations. That became a goal for this research work. This work was done for a particular city in Alberta in partnership with an Alberta-based company. This work is in the pipeline to be commercialized by that same company and it will ultimately be used by the city. A review of how blockchain technology and cryptocurrency mining operation works was performed first. A detailed methodology was proposed to create a forecasting algorithm. It included investigating which input variables, imputation techniques, data processing techniques etc. are useful for the algorithm. Six methodology cases were proposed as part of that, and python coding work was done to implement the proposed methodology. Results were generated based on that implementation and they were analyzed in detail. A final forecasting algorithm was chosen that showed good accuracy measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".