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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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PV resource evaluation based on Xception and VGG19 two-layer network algorithm.

Lifeng Li¹, Zaimin Yang¹, Xiongping Yang²

¹Energy Development Research Institute, China Southern Power Grid, China.

|November 29, 2023

Summary

This summary is machine-generated.

This study introduces a novel two-layer network algorithm combining Xception and VGG19 convolutional neural networks for enhanced photovoltaic (PV) resource assessment. The new method significantly improves the accuracy of evaluating PV resources compared to existing approaches.

Keywords:

Convolutional neural networks Double layer network algorithm Photovoltaic resource assessment Two-tier network framework

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Area of Science:

Renewable Energy Systems
Artificial Intelligence in Energy
Photovoltaic Resource Assessment

Background:

Global demand for new energy sources highlights the importance of renewable energy.
Photovoltaic (PV) energy is a key focus for sustainable power generation.
Current PV resource assessment methods lack sufficient accuracy due to single frameworks.

Purpose of the Study:

To develop a more accurate method for photovoltaic resource assessment.
To address the limitations of existing single-framework algorithms.
To improve the reliability and precision of PV energy evaluations.

Main Methods:

A novel two-layer network algorithm integrating Xception and VGG19 convolutional neural networks was developed.
The algorithm was implemented within a two-layer network framework.
Simulations were conducted to validate the proposed method's performance.

Main Results:

The proposed two-layer network algorithm demonstrated improved accuracy in PV resource assessment.
The combined Xception and VGG19 approach outperformed existing assessment algorithms.
The study verified the feasibility and reliability of the new framework.

Conclusions:

The developed two-layer network algorithm offers a significant advancement in PV resource assessment accuracy.
This AI-driven approach provides a more reliable method for evaluating photovoltaic energy potential.
The findings support the adoption of advanced deep learning techniques for renewable energy resource evaluation.