Probabilistic Electrical Load Forecasting via Prior-Guided Meta Diffusion Models
Bibliographic record
Abstract
Accurate electric load forecasting is of critical importance for modern power grids. It can help optimize energy management, reduce operational costs, and enhance grid stability. Existing load forecasting tools typically perform well when modelling long-term trends with substantive data upon which to build a model, but can perform poorly for short-term load forecasting or when data is sparse or incomplete. Diffusion models have recently emerged as powerful generative tools that excel in modelling complex distributions, making them a promising approach for electric load forecasting. In this paper, we build upon recent diffusion models for time series forecasting and explore the potential of combining diffusion models with prior models to improve performance. Additionally, we propose a metric-based meta-learning approach for fast data adaptation. Experimental results with this metric-based meta-learning approach on real-world load forecasting datasets outperform state-of-the-art baselines, showcasing the potential of diffusion-based refinement in practical forecasting applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".