Prediction of Wind Power Model Using Hybrid Method Based on WD-SVM Algorithm: Case Study Pandansimo Wind Farm

Wind power prediction with original data that has a nonstationary pattern and randomness becomes a major problem when the data is used for the preparation of the generation. Especially the wind power directly connected to the network. In this research, we proposed wavelet decomposition model and support vector machine (WD-SVM) to predict the power scale of wind power in Pandansimo wind farm. Time series data that is built in interval of 1 hour mean in 24 formed day data. The data is parsed using WD that generates the IMF component. The output of the WD model is reprocessed using the SVM model to do the clustering process which the outputs are the scale of selected wind power strength in 1 month approaching the historical data of measurement. Finally after going through the experiment, obtained the data prediction scale of wind power strength that has a high accuracy near the actual conditions. The WD-SVM hybrid model provides a smaller error than the predicted model of NN and WD-NN.