Load and locational marginal pricing prediction in competitive electrical power environment using computational intelligence
Bibliographic record
Abstract
This paper presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic optimization technique called particle swarm optimization (PSO). This training algorithm works to adjust the network weights and biases as to minimize the error function. Wavelet transformed data is fed into neural network as preprocessing stage in order to get a better price pattern that will be reliable for forecasting. The proposed models were trained and tested using real data consists of historical load and LMP and corresponding influence variables such as weather information and marginal losses cost (MLC). The data used is from NYISO and Weather Source Stations, Buffalo, New York over a period of three years (2001-2003). Simulation results are compared with that of conventional back-propagation (BP) neural network and radial basis function network (RBFN) and provided highly accurate generalization capability.
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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".