Short-Term Load Forecasting for Jordan Power System Based on NARX-ELMAN Neural Network and ARMA Model
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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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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 it