Transformer-based deep probabilistic network for load forecasting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate electric power load forecasting is critical for power utility companies as it increases control over the relevant infrastructure, resulting in significant improvements in energy management and scheduling. However, point forecasting appears to fall short of providing these businesses with enough information to prepare for the worst. This paper proposes an encoder–decoder model that takes advantage of the expressiveness of Transformer-based encoders to produce probabilistic forecasts, i.e., a distribution over future predictions. Two real-world datasets are utilized to incorporate the performance of the proposed model on two different types of data: hourly load data from the power supply company of the city of Johor in Malaysia and hourly load consumption data from one of Grenoble Institute of Technology’s buildings. The former represents aggregated data, which makes identifying patterns and trends easier, but the latter was taken from a single building (non-aggregated), which increases the difficulty of forecasts. The model’s performance is discussed across multiple time horizons, including 24-hour, 1-week, and 1-month predictions. It achieved notable improvements compared to the used baseline, Amazon DeepAr. For 24 h ahead forecasting, accuracy was increased from 87.2 percent to 96.2 percent for Malaysian data and from 52.3 percent to 68.2 percent for Grenoble data. And for 1 month ahead forecasting, it was improved from 84.7 percent to 89.7 percent for Malaysian data, and from 45.5 percent to 57.2 percent for Grenoble data.
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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 | 0.001 |
| 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 it