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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".