Application of Gray-Fuzzy-Markov Chain Method for Day-Ahead Electric Load Forecasting
Bibliographic record
Abstract
Short-term load forecasting (STLF) plays a decisive role in electric power system operation and planning. Accurate load forecasting not only reduces the generation costs of power systems, but also serves to maximize profit for participants in electricity markets. In recent years, power markets have grown more deregulated and competitive, adding to the complexity and uncertainties of load, and making it more difficult for conventional techniques to accurately forecast the load. To improve the accuracy of load forecasting, this paper suggests a hybrid method, called Gray-Fuzzy-Markov Chain Method (GFMCM), comprising three stages. In the first stage, daily load is forecasted by Gray model, with its training deviations classified, in a second stage, by fuzzy-set theory, and finally, fed into Markov chain model to predict future relative errors that might be supplied by the Gray model. The proposed approach has been verified by the historical data of power consumption in Ontario, PJM and Iranian electricity markets. The obtained forecasts by GFMCM proved to have better prediction properties compared to the other forecasting techniques, such as Gray models, specifically GM(1,1) and GM(1,2), ARIMA time series, wavelet-ARIMA and multi-layer perceptron (MLP) neural network. Streszczenie. W celu poprawy jakości przewidywania zuzycia energii autorzy zaproponowali hybrydową metode GMMCM (Gray-Fuzzy-Markov Chan Method). W pierwszym etapie prognoza obciązen jest prowadzona przy wykorzystaniu modelu Gray, nastepnie stosuje sie metody logiki rozmytej. Bląd prognozowania analizowany jest metodą Markova. (Zastosowanie kombinowanej metody: Gray-Fuzzy-Markov do prognozowania obciązenia sieci elektrycznej)
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".