Fuzzy system applications for short-term electric load forecasting.
Bibliographic record
Abstract
Load forecasting is an important function in economic power generation, allocation between plants (Unit Commitment Scheduling), maintenance scheduling, and for system security applications such as peak shaving by power interchange with interconnected utilities. In this thesis the problem of fuzzy short term load forecasting is formulated and solved. The thesis starts with a discussion of conventional algorithms used in short-term load forecasting. These algorithms are based on least error squares and least absolute value. The theory behind each algorithm is explained. Three different models are developed and tested in the first part of the thesis. The first model (A) is a regression model that takes into account the weather parameters in summer and winter seasons. The second model (B) is a harmonics based model, which does not account for weather parameters, but considers the parameters as a function of time. Model (B) can be used where variations in weather parameters are not available. Finally, model (C) is created as a hybrid combination of models A and B. The parameters of the three models are estimated using the two static estimation algorithms and are used later to predict the load for twenty-four hours ahead. The results obtained are discussed and conclusions are drawn for these models. In the second part of the thesis new fuzzy models are developed for crisp load power with fuzzy load parameters and for fuzzy load power with fuzzy load parameters. Three fuzzy models (A), (B) and (C) are developed. The fuzzy load model (A) is a fuzzy linear regression model for summer and winter seasons. Model (B) is a harmonic fuzzy model, which does not account for weather parameters. Finally fuzzy load model (C) is a hybrid combination of fuzzy load models (A) and (B). Estimating the fuzzy parameters for the three models turns out to be one of linear optimization. The fuzzy parameters are obtained for the three models. These parameters are used to predict the load as a fuzzy function for twenty-four hours ahead. Prediction results are obtained and presented using data from Nova Scotia Power and Environment Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".