Biju G. M.
ABSTRACT
This study focuses on improving the interpretability of the Long-Short-Term-Memory-Attention hybrid model applied to electrical load forecasting by optimizing its architecture. First, a temporal attention mechanism is added to the Long Short-Term Memory model to understand the temporal patterns learned by the model. Then, we introduce a novel metric assessing the model's interpretability in forecasting, gauging the temporal attention weights' ability to elucidate trends and seasonality. The optimal model architecture is then sought to maximize both interpretability and prediction accuracy, resulting in a Pareto-optimal solution representing the interpretability-accuracy trade-off. Additionally, we investigate the relationship between model architecture and interpretability.
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Index Terms
- Enhancing Interpretability of Electrical Load Forecasting with Architecture Optimization
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