Locational marginal pricing prediction in a competitve electrical market using computational intelligence
Bibliographic record
Abstract
Deregulation has created a competitive market among power market participants, and the pricing system plays an important role. Locational marginal pricing (LMP) provides clear market signals that identify the locations where power market participants could make their decisions so as to maximize their profits. In this work, artificial neural networks (ANNs) models are used to predict hourly LMP. ANN is trained using the particle swarm optimization (PSO) algorithm. PSO aims to minimize the error function by adjusting neural networkpsilas weights and biases using a stochastic optimal search. Wavelet transformed data is fed into neural network as pre-processing stage in order to get a better price pattern that will be reliable for forecasting. The historical LMP and corresponding load demand and temperature are trained, validated and tested over a period of one season. The efficient generalization of proposed model is investigated using early stopping technique. The results were compared with neural models using conventional back-propagation (BP) algorithm and radial basis function (RBF) and yielded encouraging results.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".