Short-Term Load Forecasting for Jordan Power System Based on NARX-ELMAN Neural Network and ARMA Model
Bibliographic record
Abstract
Over the past few years, there is a vast expansion of the Jordan National Energy Sector. Hence, National Electrical Power Company (NEPCO) sheds more light on load forecasting. It tries to build a rigid bridge between the academic and industrial fields. Subsequently, this work presents a study of short-term load forecasting (STLF) for the Jordanian power system. Three techniques are used: the nonlinear autoregressive exogenous model (NARX) recurrent neural network, the Elman neural network, and the autoregressive moving average (ARMA). These proposed techniques are trained, validated, and tested using the historical record of hourly load data for the whole year 2018, which is obtained from NEPCO. Besides, these techniques show a satisfactory forecasting accuracy and improve the predicted load shape performance of a week ahead (January 1, 2019, to January 7, 2019). Error is reduced based on optimizing the number of hidden layers and the number of neurons. Moreover, the mean absolute percentage errors (MAPEs) are estimated at 5.53%, 3.42%, and 10.28% for NARX, Elman, and ARMA, respectively. Finally, this work is implemented using neural network toolbox and MATLAB code in Mathworks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".