| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 107 === Because of the shortage of non-renewable energy, wind energy, which an environmentally friendly energy, has become an important alternative to fossil fuels. But the wind has the characteristics of intermittence, volatility and randomness. Wind power has brought some challenges to the stability of power system. The prediction of wind power is an important way to solve this problem. Wind speed forecasting is an important part of wind power prediction.
Under this background, this paper focuses on the following aspects of the short-term wind power prediction:
Use the time series method to build a model based on historical data, and predicts wind speed with three-hour-ahead wind speed, and establish a wind speed and power curve.
For the BP neural network, the neural network architecture with the smallest error is determined by comparing the neural networks of different structures with the same input; the input data of the wind turbine is preprocessed, and the neural network is used to determine the variable with the highest correlation with the power output.
For the support vector machine(SVM), the convergence speed is fast, the learning ability is strong, and the generalization ability is good. Even when the wind speed changes drastically, the trend of the sequence can be effectively predicted.
Finally, a combined prediction model is established, which effectively improves the prediction accuracy, and reduces the prediction error, and reduces the instability.
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