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Related Experiment Video

Updated: May 10, 2025

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

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Published on: December 15, 2023

An enhanced CNN with ResNet50 and LSTM deep learning forecasting model for climate change decision making.

Ahmed M Elshewey¹, Mona M Jamjoom², Eman H Alkhammash³

¹Department of Computer Science, Faculty of Computers and Information, Suez University, P.O. BOX: 43221, Suez, Egypt. ahmed.elshewey@fci.suezuni.edu.eg.

Scientific Reports

|April 24, 2025

Summary

Accurate climate change forecasting for temperature and wind power is vital for wind energy systems. A new hybrid deep learning model, CNN-ResNet50-LSTM, shows superior performance in predicting these factors, aiding future energy planning.

Keywords:

CNN Climate change Deep learning LSTM ResNet50 Temperature Wind power

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Using Generative Art to Convey Past and Future Climate Transitions

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Published on: March 31, 2023

Area of Science:

Environmental Science and Renewable Energy
Artificial Intelligence and Machine Learning

Background:

Climate change significantly impacts wind energy production through alterations in temperature, wind speed, and patterns.
Accurate forecasting of climatic factors is crucial for stable wind energy system operation and effective power management.
Traditional forecasting models struggle with the complex, nonlinear relationships in climate data, limiting their accuracy.

Purpose of the Study:

To develop and evaluate a hybrid deep learning model for enhanced forecasting of temperature and wind power under climate change.
To address the limitations of traditional models in capturing complex climatic data dynamics.
To provide a tool for improved wind energy system planning and management.

Main Methods:

Developed a hybrid deep learning model, CNN-ResNet50-LSTM, integrating Convolutional Neural Network (CNN), ResNet50, and Long Short-Term Memory (LSTM).
Utilized three public datasets: Wind Turbine Scada (Scada), Saudi Arabia Weather history (SA), and Wind Power Generation Data (WPG).
Evaluated forecasting accuracy using metrics like R-squared, MSE, MAE, MedAE, and RMSE, comparing against five traditional regression models.

Main Results:

The CNN-ResNet50-LSTM model achieved superior performance across all tested datasets and forecasting tasks.
Achieved R-squared scores of 98.84% (wind power, Scada), 99.01% (temperature, SA), 98.58% (temperature, WPG), and 98.35% (wind power, WPG).
Demonstrated significant improvements over traditional regression models in forecasting accuracy.

Conclusions:

The CNN-ResNet50-LSTM hybrid model is highly effective for forecasting temperature and wind power, crucial for climate change adaptation in wind energy.
The model shows strong potential for long-term climate change prediction, with applications up to 2030.
This advanced forecasting capability supports better decision-making and enhances the resilience of wind energy infrastructure.