基于云计算和数据隐私保护的风能预测方法

Lei Zhang, Shaoming Zhu, Shen Su, Xiaofeng Chen, Yan Yang, Bing Zhou
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引用次数: 0

摘要

在我国政府对能源行业承诺的支持下,风电装机容量将继续增长。然而,由于风力发电的不稳定性,准确预测风力发电量对于有效的电网调度至关重要。此外,数据隐私和保护在当今社会已变得至关重要。传统的风能预测方法依赖于集中式数据,这引发了人们对数据隐私和数据孤岛的担忧。为了应对这些挑战,我们提出了一种将联合学习和深度学习相结合的混合方法,用于风能预测。在我们提出的方法中,我们使用双向长短期记忆(BILSTM)神经网络作为基本预测模型,以提高预测精度。然后,将该模型集成到联合学习框架中,形成 Fed-BILSTM 预测方法。此外,我们还在 Fed-BILSTM 方法中引入了云计算技术,利用云资源进行模型训练和参数更新。参与者共享模型参数,而不是共享原始数据,从而解决了数据隐私问题。我们将 Fed-BILSTM 与传统预测方法进行了比较。实验结果表明,所提出的 Fed-BILSTM 在预测准确率方面优于传统预测方法。此外,与传统的集中式预测方法相比,Fed-BILSTM 能在保证预测性能的同时有效保护数据隐私。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind power prediction method based on cloud computing and data privacy protection
With the support of our government’s commitment to the energy sector, the installed capacity of wind power will continue to grow. However, due to the instability of wind power, accurate prediction of wind power output is essential for effective grid dispatch. In addition, data privacy and protection have become paramount in today’s society. Traditional wind forecasting methods rely on centralized data, which raises concerns about data privacy and data silos. To address these challenges, we propose a hybrid approach that combines federated learning and deep learning for wind power forecasting. In our proposed method, we use a bidirectional long short-term memory (BILSTM) neural network as the basic prediction model to improve the prediction accuracy. Then, the model is integrated into the federated learning framework to form the Fed-BILSTM prediction method. In addition, we have introduced cloud computing technology into the Fed-BILSTM method, using cloud resources for model training and parameter update. Participants share model parameters instead of sharing raw data, which solves data privacy concerns. We compared Fed-BILSTM with traditional forecasting methods. Experimental results show that the proposed Fed-BILSTM is better than the traditional prediction method in terms of prediction accuracy. What’s more, Fed-BILSTM can effectively protect data privacy compared to traditional centralized forecasting methods while ensuring prediction performance.
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