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Related Experiment Video

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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Reliable renewable energy forecasting for climate change mitigation.

Walid Atwa1, Abdulwahab Ali Almazroi1, Nasir Ayub2

  • 1College of Computing and Information Technology at Khulais, Department of Information Technology, University of Jeddah, Jeddah, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces an AI-powered hybrid model for accurate renewable energy generation prediction. The novel approach enhances power grid optimization and aids climate change mitigation efforts.

Keywords:
Climate changeDeep learningForecastingHybrid AI modelRenewable energy

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence
  • Climate Change Mitigation

Background:

  • Accurate prediction of electricity generation from renewable energy sources (RES) is crucial for power grid optimization and climate change mitigation.
  • Previous studies often overlooked the complex interactions between multiple energy sources, leading to inaccurate total power generation estimates.
  • Addressing these limitations requires advanced modeling techniques that capture inter-source correlations and diverse data characteristics.

Purpose of the Study:

  • To develop and validate a hybrid artificial intelligence (AI) architecture for accurate prediction of electricity generation from multiple renewable energy sources.
  • To improve the accuracy of total power generation estimations by considering the localized correlations among different energy sources.
  • To demonstrate the effectiveness of the proposed AI-infused approach in optimizing power schedules and contributing to climate change mitigation.

Main Methods:

  • A hybrid AI model integrating a gated recurrent unit (GRU) and a ResNext model, optimized using the modified Jaya algorithm (MJA).
  • Incorporation of meteorological conditions and specific energy source data, leveraging nonlinear time-series properties.
  • Application of Principal Component Analysis (PCA) to extract linear time-series characteristics for enhanced data representation.

Main Results:

  • The hybrid framework demonstrated superior accuracy compared to complex models like decision trees and ResNet.
  • Achieved low error rates for solar photovoltaic (PV) prediction, with a normalized RMSE of 6.51 and a normalized MAPE of 4.34.
  • Sensitivity analysis confirmed the importance of energy correlation patterns and the overall robustness of the AI-infused framework.

Conclusions:

  • The proposed hybrid AI model effectively predicts electricity generation from diverse renewable energy sources, outperforming existing methods.
  • The framework's ability to capture inter-source correlations and integrate various data types enhances prediction accuracy and grid stability.
  • This AI-driven approach offers a feasible and effective solution for optimizing renewable energy systems and supporting climate change mitigation goals.