A Comparison Study on Time Series Forecasting Given Smart Grid Load Uncertainties
Bibliographic record
Abstract
The trade-off between energy generation and its consumption is usually a difficult task for electricity power grids, since the data obtained from this system tied to uncertainty occasioned by seasonality and natural environment disturbances. Therefore, efforts have been made on the construction of Smart Grids, i.e. intelligent energy networks, which combine Computational Intelligence with the electricity power grids to improve the balance between energy generation and its consumption. For that, Smart Grids controllers have to be aware of future loads, and the prediction of this data must be very accurate to provide an efficient decision support. Since Smart Grid's energy consumption data varies over time, following a time series distribution, we present a comparison study over different time series forecasting in order to evaluate which one would achieve better accuracy in energy distribution. Our investigation was carried out using the Smart Grid of Ontario, Canada. The algorithms used on this experiments were the Adaptive Fuzzy Neural Network (ANFIS), Recurrent Neural Networks (RNN), Support Vector Regression (SVR), Random Forest and SARIMAX. The ANFIS algorithm had outperformed the other approaches, delivering more accurate results.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".