Peak-Load Forecasting of Nova Scotia During Winter Using Support Vector Machine with Optimally Configured Hyperparameters
Bibliographic record
Abstract
Modern machine learning (ML) computing systems proved themselves as effective tools to forecast electric load demand. However, their internal learning optimization algorithms could trap into local optima. To tackle this issue, one of the practical ways is to optimize their hyperparameters. This paper presents a simple way to do that by using the most basic random search algorithm (RSA) as a global optimizer. Support vector regression (SVR), which is a special type of support vector machine (SVM), is used. To do that, the total number of iterations is divided into multiple stages, and then RSA is sequentially executed where the search space is minimized at the end of each stage. The process ensures reaching a very rich area of near-optimal SVR settings, which can then be picked up as final candidate solutions. This technique is applied to forecast the peak-load of Nova Scotia - Canada during the winter of 2018-2019. The prediction accuracy of this effective simple technique can reach 99.89% and 97.17% for the training and test sets, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".