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Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis

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  • Charakopoulos, Avraam
  • Karakasidis, Theodoros
  • Sarris, loannis

Abstract

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 non-linear 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.

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  • Charakopoulos, Avraam & Karakasidis, Theodoros & Sarris, loannis, 2019. "Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis," Energy Policy, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:enepol:v:133:y:2019:i:c:s030142151930521x
    DOI: 10.1016/j.enpol.2019.110934
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    5. Meng, Bin & Wei, Bangguo & Yang, Mo & Kuang, Haibo, 2023. "Measuring the time-frequency spillover effect among carbon markets and shipping energy markets: A global perspective," Energy Economics, Elsevier, vol. 128(C).

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