A Novel Hybrid Method for Short-Term Probabilistic Load Forecasting in Distribution Networks
Bibliographic record
Abstract
In recent decades, the evolution of loads and distributed energy resources has added great complexity and uncertainty to distribution networks. In such a context, a reliable characterization of the uncertainty associated with prediction is fundamental to the wide range of the newly emerging applications in the low-voltage level grid. In this regard, this paper proposes a novel method to solve the short-term probabilistic load forecasting (STPLF) problem in distribution networks in which the loads are usually too volatile to be forecasted accurately. The approach developed employs a Dirichlet process mixture model (DPMM) to handle the uncertainty of load patterns, which is inferred by a Markov chain Monte Carlo (MCMC)-based method. Thereafter, the DPMM representation of the load patterns is combined with a tree-based ensemble learning method to address the STPLF by solving a classification problem. The final result is averaged over all MCMC samples. The proposed technique is compared to selected benchmark methods at different aggregation levels using smart meter datasets collected from customers located in Ireland as well as the City of Saskatoon, SK, Canada. The results obtained demonstrate that the proposed approach outperforms the benchmark methods in STPLF at the given aggregation levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".