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首页> 外文会议>Proceedings of the Fifth symposium on fractional differentiation and its applications >Fractional analysis and synthesis of the variability of irradiance and PV power time series
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Fractional analysis and synthesis of the variability of irradiance and PV power time series

机译：辐照度和PV功率时间序列变异性的分数分析和综合

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The prediction of the power output of the photovoltaic (PV) system ou000bers useful information for planning the operation and management strategy, which also helps to maintain or even to improve the power-supply reliability and quality. For this purpose, the power output of a PV system is sampled and documented for the Milagro PV plant at Navarra. The long memory indicator, the Hurst parameter H, is characterised for the power output time series of several consecutive days of October 2009. It shows that the PV power time series is a non-stationary process whose behaviour resembles a long memory process. Therefore, both the ARMA(p,q) model and the ARFIMA(p,d,q) model are built for the PV power output prediction. The orders, p and q, of the models are determined by the Akaike Information Criterion (AIC). The prediction performance of each model is quantified by the mean square errors (MSE). Comparison shows that the ARFIMA model exhibits much better prediction performance than the ARMA model at the same or even smaller orders of p and q. In addition, the original 1s-interval time series is re-sampled at 30s, 60s and 300s intervals, and the two types of models are adopted to the re-sampled time series again in order to investigate the relationship between the sampling rate and their performance.
机译：光伏（PV）系统功率输出的预测为规划运营和管理策略提供了有用的信息，这也有助于维持甚至改善电源的可靠性和质量。为此，对Navarra的Milagro光伏电站的PV系统的输出功率进行采样和记录。长存储指示器Hurst参数H代表2009年10月连续几天的功率输出时间序列。它表明PV功率时间序列是一个非平稳过程，其行为类似于长存储过程。因此，建立了ARMA（p，q）模型和ARFIMA（p，d，q）模型用于PV功率输出预测。模型的阶数p和q由Akaike信息准则（AIC）确定。每个模型的预测性能通过均方误差（MSE）进行量化。比较表明，在p和q甚至更小的阶数下，ARFIMA模型比ARMA模型表现出更好的预测性能。另外，原始的1s间隔时间序列以30s，60s和300s的间隔进行重新采样，并且再次对重新采样的时间序列采用两种类型的模型，以研究采样率与采样率之间的关系。性能。

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