Forecasting Electric Load by Aggregating Meteorological and History-based Deep Learning Modules
Bibliographic record
Abstract
Accurate day-ahead (or 24-hours ahead) electric load forecasting for power systems is crucial for system's optimal operations. In evolving smart distribution grids, the importance of precise electric load forecast in day-ahead is even more important for distributed energy management systems (DERMS) and demand response (DR) programs, which are used by the independent system operators (ISO) and power utilities (PU) for day-ahead system planning and optimal operations. This paper captures both dynamic members' interdependencies and the impact of meteorological factors on the load sequence. In this regard, Long Short-Term Memory (LSTM) is applied to use the historical load sequences to forecast the 24 hours ahead values of the system load. On the other hand, a Deep Feedforward Neural Network (DFNN) is applied to map the forecasted meteorological parameters to the upcoming 24-hourly values of the system load. Finally, based on the historical errors of these two engines, a Beta distribution generates a probabilistic weight for aggregating the forecasted values at each hour. The proposed forecasting model performs better than single engines based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metric when applied to day-ahead load forecasting for the city of Toronto.
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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 | 0.000 |
| 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".