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Wind speed forecasting based on second order blind identification and autoregressive model

机译:基于二阶盲辨识和自回归模型的风速预测

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Wind power presents undesirable intermittencies due to the considerable variations in the wind speed which may affect adversely the smooth operation of the grid. Effective wind forecast is essential in order to report the amount of energy supply with high accuracy, which is crucial for planning energy resources for power system operators. In this paper a new statistical method is presented based on independent component analysis (ICA) and autoregressive (AR) model. ICA is utilized in order to exploit the hidden factors which may exist in the wind speed time-series. ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.
机译:由于风速的显着变化,风能呈现出不希望的间歇性,这可能会不利地影响电网的平稳运行。为了准确报告能源供应量,有效的风能预测至关重要,这对于规划电力系统运营商的能源至关重要。本文提出了一种基于独立成分分析(ICA)和自回归(AR)模型的统计方法。利用ICA是为了利用风速时间序列中可能存在的隐藏因素。基于开发时间结构的ICA方法(如二阶盲识别(SOBI))可以用作风速预测的预备步骤。

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