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The effect of wind generation and weekday on Spanish electricity spot price forecasting

机译：风力发电和工作日对西班牙电力现货价格预测的影响

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摘要

This paper empirically compares the predictive accuracy of a set of methods for day-ahead spot price forecasting in the Spanish electricity market. The methods come from time series analysis and artificial intelligence disciplines, and include univariate, multivariate, linear and nonlinear. Within the univari-ate methods, the double seasonal AR1MA and the recently proposed exponential smoothing for double seasonality are compared and used as benchmarks. They allow us to quantify the improvement on price forecasting when including explanatory variables or using more complex models. Dynamic regression models including the electricity load forecast are then considered. Their good performance in price forecasting has been pointed out by many authors. However, we find evidences of their predictive accuracy can be significantly outperformed by accounting the wind generation forecast provided by the System Operator. Moreover, these forecasts can be even more accurate if changes of price's behavior according with the day of the week are taken into account by means of periodic models. The last of the tested methods are feed-forward neural networks used as multivariate nonlinear regression methods with universal function approximation capabilities. The influence of the wind generation forecast on price prediction is also proved with this approach. Detailed out-of-sample results of the tested methods are given.
机译：本文从经验上比较了西班牙电力市场中用于日间现货价格预测的一组方法的预测准确性。这些方法来自时间序列分析和人工智能学科，包括单变量，多元，线性和非线性。在单变量方法中，将双季节AR1MA和最近提出的双季节指数平滑技术进行比较，并用作基准。当包含解释变量或使用更复杂的模型时，它们使我们可以量化价格预测上的改进。然后考虑包括电力负荷预测在内的动态回归模型。许多作者指出了它们在价格预测中的良好表现。但是，我们发现，通过考虑系统操作员提供的风力发电量预测，其预测准确性的证据可能会大大优于其。此外，如果通过周期性模型考虑到价格随星期几的变化，这些预测甚至会更加准确。测试的最后一种方法是前馈神经网络，用作具有通用函数逼近功能的多元非线性回归方法。这种方法也证明了风力发电预测对价格预测的影响。给出了测试方法的详细样本外结果。

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  • 来源
    《Electric power systems research》 |2011年第10期|p.1924-1935|共12页
  • 作者

    Alberto Cruz; Antonio Munoz; Juan Luis Zamora; Rosa Espinola;

  • 作者单位

    Instituto de Investigacidn Tecnoldgica (IIT). Escuela Tecnica Superior de Ingenieria (ICAI). Universidad Pontificia de Comillas, 28015 Madrid. Spain;

    Instituto de Investigacidn Tecnoldgica (IIT). Escuela Tecnica Superior de Ingenieria (ICAI). Universidad Pontificia de Comillas, 28015 Madrid. Spain;

    Instituto de Investigacidn Tecnoldgica (IIT). Escuela Tecnica Superior de Ingenieria (ICAI). Universidad Pontificia de Comillas, 28015 Madrid. Spain;

    Escuela Universitaria de Estadistica, Universidad Complutense, 28040 Madrid, Spain;

  • 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

    electricity markets; time series analysis; electricity price forecasting; periodic models;

    机译：电力市场;时间序列分析;电价预测;周期模型;

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