Efficient community electricity load forecasting with transformer and federated learning
Bibliographic record
Abstract
Electricity data sensors are widely used across large buildings and households. As the data is collected by distributed sensors from varied locations, privacy-preserving becomes a top concern for data owners. Meanwhile, multiple deep learning models achieved state-of-art performance on forecasting with the electricity time series data in a centralized training mechanism. Although these deep learning models are powerful at capturing temporal features and making precise predictions, it usually consumes a large amount of memory and resources during the training process. To address two problems, i.e., the data privacy issue and high-demanded resources for training, we propose an efficient and practical deep learning model using a transformer framework while utilizing federated learning to move the training on local data instead of on a centralized place. With the proposed deep learning model, the computation will reduce its memory usage by 60% while achieving similar and even better results on forecasting with the electricity time series data. Case studies on the university communities’ building demonstrate our proposed solution’s great potential and comparative performance compared to the state of the arts.
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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 | 0.001 |
| 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 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".