Dual-channel encoded bidirectional LSTM for multi-building short-term load forecasting
Bibliographic record
Abstract
Buildings in a group or cluster orientation account for a rising percentage of electrical consumption causing erratic load patterns and affecting power systems efficiency at grid-level. Short-term load forecasting can provide crucial utility for effective energy transition and optimal energy management with informed decision-making at grid-level. The existing forecasting models predominantly implement deep learning techniques targeting individual buildings. This reduces the model's tendency to account for the inter-building effect while forecasting demand across multiple buildings simultaneously. Also, the existing models often overlook instantaneous peaking patterns of the buildings, leading to sub-optimal learning and predictions. Thus, to address these challenges, this study introduces a novel dual-channel encoded bi-directional long short-term memory (LSTM) load forecasting model. The proposed model is fortified with attention mechanisms, residual connections, and quantile-based metrics that specifically target the erratic peaking patterns across multiple buildings simultaneously. The proposed model is purposely architectured following an encoder-decoder scheme to extract critical local and global temporal patterns and predict electrical demand across multiple buildings simultaneously. The proposed model is tested utilizing the demand data from a Canadian university and the results are compared to the recurrent neural network-based LSTM models. The proposed model improves the average root mean square error ranging from 1.8% to 10.9% and the average mean absolute error ranging from 33.6% to 59% as compared to the existing models. • A novel LSTM network for multi-building short-term load forecasting. • Unified model to predict multi-building load simultaneously. • Use of quantile-based metrics to capture peaking patterns in load data. • Comparison with state-of-the-art LSTM models. • Improves RMSE by 1.8%–10.9% and MAPE by 33.6%–59%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".