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A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting

机译：基于支持向量机和人工智能的混合应用算法：电力负荷预测示例

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摘要

Accurate electric load forecasting could prove to be a very useful tool for all market participants in electricity markets. Because it can not only help power producers and consumers make their plans but also can maximize their profits. In this paper, a new combined forecasting method (ESPLSSVM) based on empirical mode decomposition, seasonal adjustment, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) model is proposed. In the electric market, noise signals usually affect the forecasting accuracy, which were caused by different erratic factors. First of all, ESPLSSVM uses an empirical mode decomposition-based signal filtering method to reduce the influence of noise signals. Secondly, ESPLSSVM eliminates the seasonal components from the de-noised resulting series and then it models the resultant series using the LSSVM which is optimized by PSO (PLSSVM). Finally, by multiplying the seasonal indexes by the PLSSVM forecasts, ESPLSSVM acquires the final forecasting result. The effectiveness of the presented method is examined by comparing with different methods including basic LSSVM (LSSVM), empirical mode decomposition-based signal filtering method processed by LSSVM (ELSSVM) and seasonal adjustment processed by LSSVM (SLSSVM). Case studies show ESPLSSVM performed better than the other three load forecasting approaches.
机译：准确的电力负荷预测可能被证明是电力市场中所有市场参与者的非常有用的工具。因为它不仅可以帮助电力生产商和消费者制定计划，而且可以使他们的利润最大化。本文提出了一种基于经验模式分解，季节调整，粒子群优化（PSO）和最小二乘支持向量机（LSSVM）模型的组合预测方法（ESPLSSVM）。在电力市场中，噪声信号通常会影响预测准确性，这是由不同的不稳定因素引起的。首先，ESPLSSVM使用基于经验模式分解的信号滤波方法来减少噪声信号的影响。其次，ESPLSSVM从经过去噪的结果序列中消除了季节性成分，然后使用由PSO（PLSSVM）优化的LSSVM对结果序列进行建模。最后，通过将季节性指数乘以PLSSVM预测，ESPLSSVM获得最终的预测结果。通过与包括基本LSSVM（LSSVM），由LSSVM处理的基于经验模式分解的信号滤波方法（ELSSVM）和由LSSVM处理的季节调整（SLSSVM）在内的不同方法进行比较，检验了所提出方法的有效性。案例研究表明，ESPLSSVM的性能优于其他三种负载预测方法。

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  • 来源
    《Applied Mathematical Modelling》 |2015年第9期|2617-2632|共16页
  • 作者

    Yanhua Chen; Yi Yang; Chaoqun Liu; Caihong Li; Lian Li;

  • 作者单位

    School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, PR China;

    School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, PR China;

    School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, PR China;

    School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, PR China;

    School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, PR China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Electric load forecasting; Empirical mode decomposition; Seasonal adjustment; PSO; LSSVM;

    机译：电力负荷预测;经验模式分解;季节性调整;PSO;最小二乘支持向量机;

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