Innovative Hybrid Approach for Enhanced Renewable Energy Generation Forecasting Using Recurrent Neural Networks and Generative Adversarial Networks

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Sreekumar Narayanan, Rajiv Kumar, Sudhir Ramadass, Jayaraj Ramasamy
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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.

Abstract Image

利用递归神经网络和生成式对抗网络增强可再生能源发电预测的创新混合方法
可再生能源是实现可持续绿色未来的关键,但其固有的多变性给可靠的发电预测带来了巨大挑战。针对这一关键问题,本研究提出了一种创新方法,利用递归神经网络(RNN)和生成对抗网络(GAN)的力量,彻底改变可再生能源系统的发电预测。该混合模型结合了 RNNs 和 GANs 的优势,RNNs 擅长捕捉时间动态和顺序依赖关系,而 GANs 擅长生成逼真的数据分布。结果表明,与传统方法相比,该模型显著提高了预测精度,减少了误差和不确定性。RNN-GAN 混合模型提高了可再生能源系统的可靠性,促进了可持续能源与电网的进一步融合。此外,研究还强调了并网混合系统设计和实施闭环控制框架的重要性。这些新增内容确保了预测不仅仅是理论上的,而是在实际应用中被积极用于优化能源利用和维持电网稳定。这种创新方法为实现更环保、更高效的能源环境带来了巨大希望,为向更清新、更可持续的未来过渡做出了重大贡献。所提出的混合 RNN-GAN 模型始终优于现有方法,在太阳能和风能数据方面的 RMSE 值和 MAE 值都显著降低,显示了其在可再生能源发电预测方面的卓越准确性。在 100 次迭代中,太阳能数据的 R 平方 (R2) 值为 0.82,风能数据的 R 平方 (R2) 值为 0.7,这进一步验证了该模型在捕捉潜在模式方面的有效性,而偏度和峰度分析则肯定了该模型生成符合正态分布的预测结果的能力。
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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
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.
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