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October 16, 2025Machine Learning and Knowledge ExtractionOpen Access

Hybrid Deep Learning Approaches for Accurate Electricity Price Forecasting: A Day-Ahead US Energy Market Analysis with Renewable Energy

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Authors

MRMd. Saifur RahmanHRHassan Reza

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Overview

This analysis reveals improved forecasting accuracy using hybrid deep learning approaches, indicating the significance of renewable energy integration.

Key Points

  • The VMD-BiLSTM model achieved a mean absolute error of 0.2733, demonstrating superior predictive accuracy.
  • Incorporating renewable energy impacts like temperature and wind speed enhances input feature relevance and model performance.
  • Four advanced deep learning architectures were developed to better capture complex patterns in electricity prices.
  • Data preprocessing techniques were employed to improve data quality, facilitating more accurate predictions in the energy market.

Cite This Study

Rahman et al. (2025) studied this question.

synapsesocial.com/papers/68f12bfb2107091eab27a564https://doi.org/10.3390/make7040120
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