A new approach using artificial neural network and time series models for short term load forecasting
Bibliographic record
Abstract
This paper presents a new approach for short-term load forecasting (STLF). Artificial neural network and time series models are used for forecasting hourly loads of weekdays as well as weekends and public holidays. In addition to hourly loads, daily peak load is an important data for system's operators. Most of the common forecasting approaches do not consider this issue. It is shown that the proposed approach provide very accurate forecast of the daily peak load. The input variables of the models have been selected based on their correlation coefficients. In addition, a new technique for selecting the training vectors is introduced. The valuable experience of expert operators is included in the modeling process. The model is simple, fast, and accurate. Obtained results from extensive testing on Ontario load data confirm the validity of the proposed approach. The mean percent relative error of the model over a period of one year is 2.066% including holidays.
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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".