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Enhancing Interpretability of Electrical Load Forecasting with Architecture Optimization

Published: 27 April 2024 Publication History

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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cover image ACM Conferences
ACMSE '24: Proceedings of the 2024 ACM Southeast Conference
April 2024
337 pages
ISBN:9798400702372
DOI:10.1145/3603287
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 27 April 2024

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Author Tags

  1. Artificial Intelligence
  2. Attention
  3. Deep Learning
  4. Electrical Load Forecasting
  5. Hyperparameter Optimization
  6. Interpretability
  7. Long Short-Term Memory
  8. Pareto Optimization
  9. Time Series Analysis

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ACM SE '24
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ACM SE '24: 2024 ACM Southeast Conference
April 18 - 20, 2024
GA, Marietta, USA

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ACMSE '24 Paper Acceptance Rate 44 of 137 submissions, 32%;
Overall Acceptance Rate 502 of 1,023 submissions, 49%

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