EM Algorithm for Electricity Pool Price Prediction and Errors-in-variables Process Identification
Bibliographic record
Abstract
In this thesis, under the EM algorithm framework, a multiple model approach is developed towards electricity price prediction, and the identification problem for errors-in-variables (EIV) systems is studied. Alberta's electricity price, which shows high volatility and erratic nature, is considered as an example of a nonlinear process. A Markov regime-switching model is applied to predict the price using the local models through the investigations of characteristics of the pool price sequence. The expectation maximization (EM) algorithm is applied to solve the maximum likelihood (ML) estimation problem for model parameters, and several initialization methods are proposed to generate the initial values for the EM algorithm. The validations are presented to verify the proposed approach, which demonstrate an improvement on the existing price prediction for the range of high electricity prices. A dynamic system that has both input and output measurement errors is considered as an errors-in-variables (EIV) system. Employment of traditional identification strategies for EIV systems will result in biased estimates and inaccurate estimation of system parameters. EIV approaches such as the subspace EIV method has been proposed, but the subspace approach does not possess the optimality such as ML estimation. However, the direct application of ML approach for EIV model parameter estimation can lead to intractable solutions. In this work, we assume a dynamic model for noise-free input and propose to solve the ML problem using the EM algorithm. To identify industrial nonlinear EIV processes that operate along an operating trajectory, a linear parameter varying (LPV) EIV model is proposed to approximate the global models. The EM algorithm is used to solve the ML estimation for LPV EIV model parameters. Various numerical simulations and pilot-scale experiments are used to demonstrate the effectiveness of the proposed approach.
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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 | 0.001 |
| 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".