Adaptive filter-driven optimized attention-based CNN-LSTM for load forecasting in microgrids
Bibliographic record
Abstract
Load forecasting in microgrids enables efficient balancing of supply and demand, ensuring that energy generation, storage, and consumption are optimally coordinated. A major challenge in load forecasting in microgrids is that the optimal model for one grid may not be the best for another, considering each system's different characteristics. Based on this concern, this paper proposes a data-driven adaptive filter, ensuring that the model can be applied to any microgrid. For model tuning, the adaptive tree-structured Parzen estimator (ATPE) was shown to be more efficient in finding the optimal hyperparameters than random search, annealing search, and TPE optimization strategies. The proposed hybrid prediction method integrates an adaptive filter (AF) input stage into an optimized attention-based (OA) convolutional neural network with long short-term memory (CNN-LSTM). Based on that, the model features a data-driven AF that automatically adjusts its denoising hyperparameter based on the input signal's sampling rate, ensuring robust performance across diverse datasets without manual tuning. When evaluated on three microgrid datasets (Liege, Technical University of Ostrava, and Rye), the proposed AF-OA-CNN-LSTM model demonstrated top performance compared to state-of-the-art deep learning architectures. Achieving an RMSE of 0.00116 (3.44% better than DeepAR and 239.65% better than TimesNet, the 2nd and 3rd best models, respectively) and a MAPE of 1.15% (296.52% better than TFT and 313.04% better than TimesNet, the 2nd and 3rd best models, respectively) in the best case (Liege dataset), the proposed method is a promising generalizable solution for load forecasting in different load contexts. • Propose an adaptive filtered attention-based CNN-LSTM model optimized via ATPE. • Developed a data-driven adaptive filter generalizable for load forecasting. • APTE for hypertuning compared to random search, annealing search, and TPE. • The hybrid approach achieved superior performance across three load datasets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".