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首页> 外文期刊>Energy Policy >Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis
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Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis

机译：通过复杂网络进行风电预测的模式识别和递归图时间序列分析

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摘要

Renewable energy sources, where wind energy is an important part, are increasingly participating in developing economies and environmental benefits. Wind power is strongly dependent on wind velocity and thus identifying patterns in wind speed data is an important issue for forecasting the generated power from a wind turbine and it has significant importance for the renewable energy market operations. In this work we approach the problem of identification of the underlying dynamic characteristics and patterns of wind behavior using two approaches of nonlinear time series analysis tools: Recurrence Plots (RPs) and Complex Network analysis. The proposed methodology is applied on wind time series collected by cup anemometers located on a wind turbine installed in Greece. We show that the proposed approach provides useful information which can characterize distinct two time intervals of the data, one ranging from 2 to 4.5 days and another from 5 to 8.5 days. Also analysis can identify and detect dynamical transitions in the system's behavior and also reveals information about the changes in state inside the whole time series. The results will be useful in wind markets, for the prediction of the produced wind energy and also will be helpful for wind farm site selection.
机译：在风能中很重要的可再生能源正在越来越多地参与发展中经济体和环境利益。风力强烈依赖于风速，因此，识别风速数据中的模式是预测风力涡轮机发电量的重要问题，并且对可再生能源市场运营至关重要。在这项工作中，我们使用非线性时间序列分析工具的两种方法来解决识别基本动态特征和风行为模式的问题：递归图（RP）和复杂网络分析。拟议的方法适用于安装在希腊的风力涡轮机上的风速计收集的风时间序列。我们表明，所提出的方法提供了有用的信息，可以表征数据的两个不同的时间间隔，一个从2到4.5天不等，另一个从5到8.5天不等。分析还可以识别和检测系统行为的动态转变，还可以揭示有关整个时间序列内状态变化的信息。该结果将对风能市场有用，可用于预测产生的风能，也有助于风电场选址。

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