Regional Ultra-Short-Term Wind Power Combination Prediction Method Based on Fluctuant/Smooth Components Division

Abstract

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When multiple scattered wind farms are connected to the power grid, the meteorological and geographic information data used for power prediction of a single wind farm are not suitable for the regional wind power prediction of the dispatching department. Therefore, based on the regional wind power historical data, this study proposes a combined prediction method according to data decomposition. Firstly, the original sequence processed by the extension methods is decomposed into several regular components by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). All the components are classified into two categories: fluctuant components and smooth components. Then, according to the characteristics of different data, the long short-term memory (LSTM) network and autoregressive integrated moving average (ARIMA) model are used to model the fluctuant components and the smooth components, respectively, and obtain the predicted values of each component. Finally, the predicted data of all components are accumulated, which is the final predicted result of the regional ultra-short-term wind power. The feasibility and accuracy of this method are verified by the comparative analysis.

Keywords

• wind power prediction
• CEEMDAN
• LSTM network
• ARIMA
• combination prediction