Locational Marginal Price Forecasting Based on Deep Neural Networks and Prophet Techniques
Bibliographic record
Abstract
In many of the electricity markets in North America, the electricity prices are in terms of the locational marginal price (LMP) which reflects the cost of supplying the next MWh of electricity at a bus, considering transmission constraints. Electricity price forecasting provides vital information on system conditions to the independent system operator (ISO). It indicates important signals pertaining to the need of investing in the new generation, upgrading transmission, or reducing electricity consumption. Power suppliers and consumers use the forecasted price to optimize the profit in the day-ahead market and bilateral contracts. Facility owners rely on the forecasted price to make investment decisions. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are applied to improve short-term (time series) LMP forecasting accuracy. Dataset from the ISO-NE power market is utilized in modeling and analysis in which the proposed techniques are applied using Matlab and Python software. Various methods are evaluated and compared, and the conclusions achieved show that LSTM has lower error rates and higher accuracy than the Prophet forecasting model in 24 h-ahead LMP forecasting.
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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".