Federated and Multi-Task Learning for Privacy-Preserving Short-Term Electric Load Forecasting
Bibliographic record
Abstract
Electric load forecasting involves gathering and analyzing load data from end-users to help utilities maintain the balance between supply and demand, plan and manage their infrastructure, and price their services.Privacy concerns arise from sharing electric load data.Federated Learning (FL) entails training a global model among several clients by sharing model weights instead of data.Using multiple server aggregation algorithms, we implement several fine-tuning (personalizing) optimizers to improve the local load forecasting accuracy compared to non-personalized FL.We also propose training a Temporal Fusion Transformer (TFT), a type of deep learning model that combines the strengths of transformers and LSTM networks, via FL, which improved load forecasting accuracy and reduced communication and computation costs compared to other common deep learning models.Recommendations are presented regarding several TFT architecture hyperparameters to improve load forecasting accuracy and reduce costs even further.Enforcing and relaxing lockdown rules and changing habits of people caused major uncertainty in load forecasting during the Coronavirus disease of 2019 (COVID-19).To overcome this, we propose two disaster-aware multi-task learning (MTL) models for residential and city levels load forecasting.The proposed MTL models allowed learning from two different datasets with different features, and reduced load forecasting error for both levels compared to a non-MTL approach.Firstly, I would like to express my sincere gratitude to my supervisor, Professor Mohamed Ibnkahla, for giving me the opportunity to research and experiment on such an interesting and trending topic.I would also like to thank him for the support and guidance along the way in my Master's degree, as well as my Bachelor's capstone project.I would like to thank Dr. Zied Bouida for all the
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".