Improved Forecasting of Short Term Electricity Demand by using of Integrated Data Preparation and Input Selection Methods
Bibliographic record
Abstract
The main aim of this paper is to emphasize on the significant role of data pre-processing phase in improving the short-term load demand forecasting. Different transformation approaches including normalization, Zscore and Box-Cox method are applied and various input selection methods including forward selection, backward selection, stepwise regression and principle component analysis are used to see how the combination of these pre-processing techniques will influence the performance of different parametric (ARIMA, ARIMAX, MLR) and non-parametric (NAR, NARX, SVR, ANFIS) predictors. The data was collected from the daily load demand of Ottawa, Canada. It was observed that the Box-Cox transformation significantly improved the performance of all predictors and the findings demonstrated the superior role of exogenous variables in accuracy improvement of all predictors. In terms of MAPE, the value of 2.27% for ARIMA model improved to 1.75% with ARIMAX using temperature, and it decreased from 1.46% to 1.334% by means of NARX model using normalized PCA which is applied on normalized data. In an overall view, the non-parametric algorithms had a considerable gain over parametric models and NARX network had the highest accuracy among all of the predictors.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".