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  1. Home
  2. Esg Guidance And Artificial Intelligence Support For Power Systems Analytics In The Energy Industry.
  1. Home
  2. Esg Guidance And Artificial Intelligence Support For Power Systems Analytics In The Energy Industry.

Related Experiment Video

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ESG guidance and artificial intelligence support for power systems analytics in the energy industry.

Qingjiang Li1, Guilin Zou2, Wenlong Zeng3

  • 1China Southern Power Grid Co., Ltd., Guangzhou, 510000, People's Republic of China.

Scientific Reports
|May 18, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study integrates artificial intelligence (AI) and environmental social governance (ESG) for advanced power system analysis. It introduces novel models for load demand forecasting and fault diagnosis, enhancing efficiency and accuracy in the energy sector.

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Area of Science:

  • Power Systems Engineering
  • Artificial Intelligence
  • Environmental Social Governance (ESG)

Background:

  • Power system analysis and fault diagnosis require enhanced precision and effectiveness.
  • Integrating Environmental Social Governance (ESG) is crucial for assessing power system impacts.
  • Existing methods may lack accuracy in load demand forecasting and fault prediction.

Purpose of the Study:

  • To assess power systems using AI and ESG for improved analysis and fault diagnosis.
  • To develop a CNN-BiLSTM model for accurate power load demand forecasting.
  • To implement and optimize a Deep Belief Network (DBN) with Particle Swarm Optimization (PSO) for power grid fault diagnosis.

Main Methods:

  • Presentation of an ESG framework to evaluate environmental, social, and governance impacts.
  • Development of a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model for load demand forecasting.
  • Optimization of a Deep Belief Network (DBN) using Particle Swarm Optimization (PSO) for fault diagnosis.
  • Main Results:

    • The CNN-BiLSTM model significantly improved forecasting accuracy with low RMSE (0.054), MAE (0.076), and MAPE (0.102).
    • The PSO-optimized DBN achieved 96.22% accuracy in power grid fault diagnosis within 129.94 seconds.
    • The proposed models outperformed existing algorithms in both forecasting and fault prediction efficiency.

    Conclusions:

    • The integrated AI and ESG approach enhances power system analysis and fault diagnosis.
    • The study provides a robust framework for sustainable and intelligent growth in the energy industry.
    • The developed models offer significant improvements in accuracy and efficiency for power system operations.