Short term load forecasting (STLF) is an important process in electric power operation and control system. Support Vector Regression (SVR) is proved to be a successful application in STLF, and can get great accuracy and efficiency compared to other STLF models. However, when deal large scale sample size, SVR is poor on the performance. With the development of cloud computing, it is changing people's life in more and more areas. Windows Azure Platform is a cloud computing platform developed by Microsoft. It can easily scale up or down to get compute or storage resource according to requirements. Take into account the advantage and convenience, we propose a parallel SVR model based on Windows Azure Platform to solve the large scale dataset problem of SVR. This model is verified with ENUN standard dataset, the results shows that the model of SVR based on Windows Azure Platform has apparently improvement on efficiency than standard SVR model.
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机译：短期负荷预测（STLF）是电力运行和控制系统中的一个重要过程。证明支持向量回归（SVR）是在STLF中的成功应用程序，与其他STLF模型相比，可以获得很高的准确性和效率。但是，当交易大规模样本大小时，SVR对性能差。随着云计算的发展，它正在越来越多地改变人们的生命。 Windows Azure平台是Microsoft开发的云计算平台。它可以轻松地向上或向下扩展以根据要求获取计算或存储资源。考虑到优势和方便，我们提出了一种基于Windows Azure平台的并行SVR模型来解决SVR的大规模数据集问题。该模型通过ENUN标准数据集进行了验证，结果表明，基于Windows Azure平台的SVR模型显然对效率的提高而不是标准SVR模型。
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