September 10, 2025Open Access

A Hybrid Deep Learning Model for Wind and Solar Power Forecasting in Smart Grids

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This analysis demonstrates improved forecasting accuracy in smart grids by integrating deep learning models for wind and solar power.

The proposed hybrid deep learning model significantly improves forecasting accuracy for renewable energy sources.
Experimental results show that hybrid CNN and LSTM outperform traditional forecasting methods.
This model effectively addresses the complexities of wind and solar power generation through innovative feature extraction.
The findings highlight the potential for enhanced smart grid management and renewable energy integration.

Fozlur Rayhan (2025) studied this question.

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September 10, 2025Open Access

View Full Paper

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Authors

This analysis demonstrates improved forecasting accuracy in smart grids by integrating deep learning models for wind and solar power.

The proposed hybrid deep learning model significantly improves forecasting accuracy for renewable energy sources.
Experimental results show that hybrid CNN and LSTM outperform traditional forecasting methods.
This model effectively addresses the complexities of wind and solar power generation through innovative feature extraction.
The findings highlight the potential for enhanced smart grid management and renewable energy integration.

Fozlur Rayhan (2025) studied this question.

View Full Paper

Ask AI

Bookmark