|Analysis of wind power forecasting technologies
|Xu, Zhao (EE)
|Wind power -- Forecasting.
Wind power plants -- Forecasting.
Hong Kong Polytechnic University -- Dissertations
|Department of Electrical Engineering
|v, 53 pages : illustrations (some color)
|In order to get the electricity without pollution in the future, the use of wind power has made significant advances. But in electric power system, the power networks of wind farms integration have become a problem for the commitment unity and power plants control. As everyone knows wind is one of the weather variables that it is difficult to predict. Intermittent in nature, it is hard to forecast the electricity produced of short-term in a wind farm. It is even difficult in general, in the next few hours any advantages get from the wind farms in not best, and may be essential to improve the spinning reserve of power plant. Hence, it is necessary to administrate energy resources and the alternative energy advent, especially wind power, reduced to the use for short-term forecast of advanced tools of wind speed or some same thing, the wind production. It will start from time series prediction method, brief introduce the basic of standards predicted and the model of time sequence. There are two powerful and useful tools to describe an individual time series of the dynamics, called The Auto Regressive Moving Average (ARMA) model and Artificial Neural Networks (ANN) model. ARMA model can only provide point predict, and neural networks provide both point and interval predict by employ the bootstrap method.
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