Short-Term Load Forecasting in a microgrid environment: Investigating the series-specific and cross-learning forecasting methods
Bibliographic record
Abstract
Abstract A reliable and accurate load forecasting method is key to successful energy management of smart grids. Due to the non-linear relations in data generating process and data availability issues, load forecasting remains a challenging task. Here, we investigate the application of feed forward artificial neural networks, recurrent neural networks and crosslearning methods for day-ahead and three days-ahead load forecasting. The effectiveness of the proposed methods is evaluated against a statistical benchmark, using multiple accuracy metrics. The test data sets are high resolution multi-seasonal time series of electricity demand of buildings in Belgium, Canada and the UK from private measurements and open access sources. Both FFNN and RNN methods show competitive results on benchmarking datasets. Best method varies depending on the accuracy metric selected. The use of cross-learning in fitting a global RNN model has an improvement on the final accuracy.
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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 | 0.001 | 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 | 0.001 |
| 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".