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Long Term Solar Radiation Forecast Using Computational Intelligence Methods

机译:基于计算智能方法的长期太阳辐射预报

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摘要

The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR) filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour.
机译:点预测质量与解释观测过程动态的模型密切相关。有时可以通过简单的代数方程获得模型,但是在大多数物理系统中,使用简单的常微分或差分方程很难建立相关的现实。在具有非线性或非平稳行为的系统中,需要更复杂的模型。通过对太阳辐射进行采样而获得的离散时间序列问题可以在这种情况下进行描述。通过观察收集的数据,可以区分多种情况。另外,由于诸如云的大气干扰,样本之间的时间结构很复杂,最好用非线性模型来描述。本文通过结合支持向量回归范式和马尔可夫链的混合模型报告太阳辐射的预测。将混合模型的性能与使用其他方法(例如自回归(AR)滤波器,Markov AR模型和人工神经网络)获得的性能进行比较。获得的结果表明,关于预测误差和动态行为,混合模型的预测性能都有所提高。

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  • 来源
    《Applied computational intelligence and soft computing》 |2014年第2014期|729316.1-729316.14|共14页
  • 作者单位

    ESTiG, Instituto Politecnico de Braganca, Campus de Santa Apolonia, Apartado 1134, 5301-857 Braganca, Portugal,Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Portugal;

    Universidade de Tras-os-Montes e Alto Douro (UTAD), Escola de Ciencias e Tecnologia, Vila Real, Portugal,INESC Technology and Science (INESC TEC), Portugal;

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