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重要过程。支持向量回归（SVR）在STLF中被证明是成功的应用程序，与其他STLF模型相比，它可以提高准确性和效率。但是，当处理大规模样本量时，SVR的性能较差。随着云计算的发展，它在越来越多的领域改变着人们的生活。 Windows Azure平台是由Microsoft开发的云计算平台。它可以根据需要轻松扩展或缩小以获取计算或存储资源。考虑到优点和便利性，我们提出了一种基于Windows Azure平台的并行SVR模型，以解决SVR的大规模数据集问题。通过ENUN标准数据集对该模型进行了验证，结果表明，基于Windows Azure平台的SVR模型比标准SVR模型在效率上有明显提高。
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