Modeling and prediction of wind power data
In a power system, supply and demand must be equal at all times. The wind power forecast accuracy is directly connected to the need for balancing energy and hence to the cost of wind power integration. Many ways are used to predict wind power, but the accuracy of the predictions is not as good as we expect. Thus, as levels of wind penetration into the electricity system increase, new methods of balancing supply and demand are necessary. The goal of this research is to develop numerical methods for prediction and parameter estimation for complex problem such as wind power prediction. We used different time series models in statistics to predict wind power, including ARIMA model, SARIMA model, ARAR model, Holt-Winters method, and a state-space model. We compared the difference between the predicted data and the original data. We conclude that a state space model incorporating trend and seasonal variables, a Kalman prediction filter, with the parameters of the model estimated by maximizing the likelihood function is the most powerful method for prediction.
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