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Multi-Energy Load Prediction Method for Integrated Energy System Based on Fennec Fox Optimization Algorithm and

Yang Shen1, Deyi Li2, Wenbo Wang2

  • 1College of Science, Wuhan University of Science and Technology, Wuhan 430081, China.

Entropy (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

Accurate multi-energy load forecasting for integrated energy systems (IES) is achieved using a novel approach combining the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine for enhanced energy sustainability.

Keywords:
comprehensive weight methodfennec fox optimization algorithmhybrid kernel extreme learning machineintegrated energy systemmulti-energy load prediction

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Area of Science:

  • Energy Systems Engineering
  • Artificial Intelligence
  • Forecasting Methodologies

Background:

  • Integrated energy systems (IES) are crucial for energy sustainability but face challenges in accurate load prediction due to fluctuating loads and numerous influencing factors.
  • Reliable multivariate load forecasting is essential for optimal scheduling and stable operation of IES.

Purpose of the Study:

  • To develop an advanced multi-energy load prediction approach for IES.
  • To enhance the accuracy and reliability of load forecasting in complex energy systems.

Main Methods:

  • A comprehensive weight method combining entropy weight and Pearson correlation coefficient was used to select key predictive factors.
  • A hybrid kernel extreme learning machine (ELM) was employed to model the coupling relationship between multi-energy loads.
  • The fennec fox optimization algorithm (FFA) was utilized for optimizing ELM parameters, reducing prediction randomness.

Main Results:

  • The proposed method demonstrated high accuracy in multi-energy load forecasting, validated with data from Arizona State University.
  • Achieved Mean Absolute Error (MAE) of 0.0959, 0.3103, and 0.0443.
  • Achieved Root Mean Square Error (RMSE) of 0.1378, 0.3848, and 0.0578, with a Weighted Mean Absolute Percentage Error (WMAPE) of 1.915%.

Conclusions:

  • The combined FFA and hybrid kernel ELM approach significantly improves multi-energy load forecasting accuracy for IES.
  • The method offers substantial reductions in MAE, RMSE, and WMAPE compared to other models.
  • This approach provides a reliable tool for optimizing IES operation and advancing energy sustainability.