Load Forecasting using GNN-LSTM Attention Mechanism with Low-Frequency Data
Bibliographic record
Abstract
Non-intrusive load monitoring (NILM) in smart home applications provides insights into household energy usage patterns. This paper presents a NILM methodology using a graph neural network (GNN), a long short-term memory (LSTM) network, and an attention mechanism. It also investigates the temporal correlation between household appliances. The objective of the proposed learning-based approach is to represent the complex dependencies between appliances and their power usage. We capture the interaction between appliances in the form of a graph by integrating a GNN, which serves as the foundation for more effective feature extraction. An LSTM network is then implemented to capture temporal patterns. The attention process, on the other hand, focuses on the most crucial information to further boost the prediction performance. We utilize heat maps to develop a better understanding of how household appliances perform and correlate over time. These visualizations provide insight into usage patterns and power consumption sequences by giving information about the temporal interconnections between appliances. Experimental results demonstrate the effectiveness of the proposed approach in accurate load prediction by uncovering hidden patterns within household appliance data.
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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 | 0.000 |
| 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".