Short-term Electricity Price Forecast of Electricity Market Based on E-BLSTM Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The price changes in the electricity market will adversely affect the utility revenue and user cost. The current electricity price forecasting method has a low degree of utilization of its periodic variation law and a short forecast step size, which makes the electricity price forecast have large errors. A two-way LSTM model based on ELU activation function is proposed to predict the short-term electricity price change on the supply side of the power market. The gradient disappearance problem in the back propagation calculation process is optimized by ELU, and the accuracy of electricity price prediction is improved. Experiments on models and algorithms in the electricity price database of the PJM power market in the United States show that compared with the ARIMA and ARMA models, the proposed model has higher accuracy, and the algorithm converges to a lower loss rate, which can provide greater fluctuations in the supply side of the power market. The electricity price is accurately predicted.
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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