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Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network.

Alper Ozcan1, Cagatay Catal2, Ahmet Kasif3

  • 1Department of Computer Engineering, Akdeniz University, Antalya 07070, Turkey.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
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This study introduces a new deep learning model for accurate short-term load forecasting (STLF). The Dual-Stage Attention-Based Recurrent Neural Network improves energy load prediction by capturing complex temporal dependencies.

Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Ensuring a stable, affordable, and safe energy supply is a significant challenge for utility providers.
  • External factors like weather and market volatility impact energy services, necessitating accurate forecasting for proactive measures.
  • Electrical load forecasting is a critical time series prediction problem for grid management.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for enhancing short-term load forecasting (STLF) accuracy.
  • To address limitations in existing time series prediction methods for electrical load data.
  • To improve the reliability and efficiency of energy service provision through better load predictions.

Main Methods:

  • Proposed a novel Dual-Stage Attention-Based Recurrent Neural Network (DRNN) model.
Keywords:
dual-stage attention-based recurrent neural networkenergy consumption predictiontime series forecasting

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  • Incorporated attention mechanisms in both the encoder and decoder stages of the DRNN.
  • Utilized the UCI Household Electric Power Consumption (HEPC) dataset for model evaluation.

    • Main Results:

    • The proposed DRNN model demonstrated superior performance in short-term load forecasting.
    • Achieved improved accuracy metrics, specifically lower Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE).
    • Experimental results indicated that the DRNN model outperformed existing techniques.

    Conclusions:

    • The Dual-Stage Attention-Based Recurrent Neural Network is effective for short-term load forecasting.
    • The model's attention mechanisms enhance its ability to capture temporal dependencies and improve prediction accuracy.
    • This research contributes to more reliable and efficient energy management systems.