Improving the Efficiency of Renewable Energy Systems using Intelligent Control Algorithms based on Deep Learning

This paper proposes an intelligent control model utilizing deep learning techniques to enhance the efficiency of renewable energy systems, specifically focusing on a hybrid solar and wind energy system. The model aims to address challenges related to energy production fluctuations due to weather conditions, ultimately reducing energy losses. Performance evaluations will compare this algorithm with existing systems to demonstrate its effectiveness

Publication Details

Journal
PhilPapers (PhilPapers Foundation)
Published
2025-01-01
Primary Topic
Electromagnetic Compatibility and Measurements
Type
article
Field-Weighted Citation Impact
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article

Improving the Efficiency of Renewable Energy Systems using Intelligent Control Algorithms based on Deep Learning

PhilPapers (PhilPapers Foundation)
Electromagnetic Compatibility and Measurements
article

Improving the Efficiency of Renewable Energy Systems using Intelligent Control Algorithms based on Deep Learning

article en

Abstract

This paper proposes an intelligent control model utilizing deep learning techniques to enhance the efficiency of renewable energy systems, specifically focusing on a hybrid solar and wind energy system. The model aims to address challenges related to energy production fluctuations due to weather conditions, ultimately reducing energy losses. Performance evaluations will compare this algorithm with existing systems to demonstrate its effectiveness

PhilPapers (PhilPapers Foundation)
Affordable and clean energy
Openalex Percentile: Top 50%
Electromagnetic Compatibility and Measurements
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Improving the Efficiency of Renewable Energy Systems using Intelligent Control Algorithms based on Deep Learning · PhilPapers (PhilPapers Foundation) (2025) | TGRS Research Map | TGRS