{"title":"Innovative Hybrid Approach for Enhanced Renewable Energy Generation Forecasting Using Recurrent Neural Networks and Generative Adversarial Networks","authors":"Sreekumar Narayanan, Rajiv Kumar, Sudhir Ramadass, Jayaraj Ramasamy","doi":"10.1007/s42835-024-01943-3","DOIUrl":null,"url":null,"abstract":"<p>Renewable energy sources hold the key to a sustainable and green future, yet their inherent variability poses significant challenges for reliable power generation forecasting. In response to this critical issue, this study presents an innovative approach that harnesses the power of both Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs) to revolutionize power generation forecasting in renewable energy systems. The hybrid model combines the strengths of RNNs, known for capturing temporal dynamics and sequential dependencies, and GANs, renowned for generating realistic data distributions. The results demonstrate a remarkable improvement in forecasting accuracy compared to traditional methods, reducing errors and uncertainties. The hybrid RNN-GAN model enhances the reliability of renewable energy systems, facilitating greater integration of sustainable energy sources into the grid. Furthermore, the research underscores the importance of incorporating a Grid-Connected Hybrid System Design and implementing a closed-loop control framework. These additions ensure that the forecasts are not just theoretical but are actively used to optimize energy utilization and maintain grid stability in real-world scenarios. This innovative approach holds great promise for a greener and more efficient energy landscape, making a substantial contribution to the transition towards a fresher and more sustainable future. The proposed Hybrid RNN-GAN model consistently outperforms existing methods, yielding significantly lower RMSE and MAE values for both solar and wind data, showcasing its superior accuracy in renewable energy generation forecasting. The achieved R-squared (R<sup>2</sup>) values of 0.82 for solar data and 0.7 for wind data at 100 iterations further validate the model's effectiveness in capturing underlying patterns, while skewness and kurtosis analyses affirm its ability to generate predictions aligned with normal distributions.</p>","PeriodicalId":15577,"journal":{"name":"Journal of Electrical Engineering & Technology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Engineering & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42835-024-01943-3","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Renewable energy sources hold the key to a sustainable and green future, yet their inherent variability poses significant challenges for reliable power generation forecasting. In response to this critical issue, this study presents an innovative approach that harnesses the power of both Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs) to revolutionize power generation forecasting in renewable energy systems. The hybrid model combines the strengths of RNNs, known for capturing temporal dynamics and sequential dependencies, and GANs, renowned for generating realistic data distributions. The results demonstrate a remarkable improvement in forecasting accuracy compared to traditional methods, reducing errors and uncertainties. The hybrid RNN-GAN model enhances the reliability of renewable energy systems, facilitating greater integration of sustainable energy sources into the grid. Furthermore, the research underscores the importance of incorporating a Grid-Connected Hybrid System Design and implementing a closed-loop control framework. These additions ensure that the forecasts are not just theoretical but are actively used to optimize energy utilization and maintain grid stability in real-world scenarios. This innovative approach holds great promise for a greener and more efficient energy landscape, making a substantial contribution to the transition towards a fresher and more sustainable future. The proposed Hybrid RNN-GAN model consistently outperforms existing methods, yielding significantly lower RMSE and MAE values for both solar and wind data, showcasing its superior accuracy in renewable energy generation forecasting. The achieved R-squared (R2) values of 0.82 for solar data and 0.7 for wind data at 100 iterations further validate the model's effectiveness in capturing underlying patterns, while skewness and kurtosis analyses affirm its ability to generate predictions aligned with normal distributions.
期刊介绍:
ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies.
The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.