近年來在這個凡事講求環保的社會中，零污染再生能源已被世界各所重視，而風能是種可再生且潔淨的能源，因此得到了廣泛的使用。而風能的預測也變得十分的重要，準確的預測風能可以降低成本提升品質，故對風力公司或電力公司都是十分的重要，然而因為風的隨機性與不穩定性，所以要準確預測風能的是有極大的難度，然而，現代人為了預測風能建立了許多不同的數學模型，根據方法可分成兩大種類，一是物理性模型，而另一種是統計型模型，本研究將建立一整合型模型以預測風能。
在此研究中，我們建立一整合型模型來預測風能，首先我們利用HHT將已知數據切分成不同頻率的部分，將高頻區(風機無法反映)刪去，並考慮TIME-LAG的部分，再利用多項式函數系統辨別法 (polynomial system identification method )利用黑盒子模型(black-box model)為基礎，在加上一些已知的知識(白盒子模型white-box model)，成功建立風速-風機輸出功率數學模型，未來可以應用於台灣離岸風機健康監控(health monitoring)及風能預測(wind energy forecast)上。

The variable nature of wind energy has resulted in several difficulties for grid operators. One challenging problem is the prediction of the output power of wind farms. A polynomial approach of predicting the power output of generic wind power plants is presented. This research establishes a constructive approach for finding the mathematical model that captures the input-output properties of generic wind power plants. We first select related information as the candidate input data using statistical methods. The input-output relationship of wind power plants are modeled as a polynomial system with time-delay. The coefficients of the polynomial system are identified using various system identification methods. Data obtained from simulation software and real wind power plants are used in this research. These data include wind related data and information available from generic wind power plants. The related input data are decomposed into several intrinsic mode functions using Hilbert-Huang-Transform. The intrinsic mode functions are cross correlated with the output power to identify the significant components as well as the possible delay time. The relationship between the related intrinsic mode functions and the output power is modeled as a polynomial system with time-delay to model the nonlinear properties of wind power plants. Several simulation results are provided to show the effectiveness of the proposed approach.

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