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Machine learning approaches to predicting energy price correlation: From a responsible AI perspective

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  • Su, Yu
  • Feng, Xuan

Abstract

This study is positioned within Responsible AI practice in energy markets, which exhibit inherent volatility and complexity. We integrate classical and modern machine learning techniques for enhanced energy price correlation forecasting. Principal Component Analysis (PCA) is employed for dimensionality reduction to identify underlying factors driving energy price correlations, leveraging its interpretability as a key analytical advantage. Long Short-Term Memory (LSTM) networks are then introduced for time-series modeling of energy prices and their inter-correlations.

Suggested Citation

  • Su, Yu & Feng, Xuan, 2026. "Machine learning approaches to predicting energy price correlation: From a responsible AI perspective," Technological Forecasting and Social Change, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:tefoso:v:225:y:2026:i:c:s0040162525005463
    DOI: 10.1016/j.techfore.2025.124515
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