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In this paper, a novel ensemble method consisting of neural networks, wavelet transform, feature selection, and partial least-squares regression (PLSR) is proposed for the generation forecasting of a wind farm. Based on the conditional mutual information, a feature selection technique is developed to choose a compact set of input features for the forecasting model. In order to overcome the nonstationarity...
Capacity and output power forecasting have great significance in seamless integration of renewable energy to the grid. However, the uncertainty of wind power and intermittence of wind energy are the main factors which affect forecasting precision. The wind power output data can be treated as a signal stream which has characteristics for possible wind capacity forecasting. Hilbert-Huang Transforms...
Short term wind power prediction is one of the effective ways to cope with the operational problems caused by the variability of the wind energy resource when a large penetration of wind power is integrated into electric power systems. In this paper, a model combining a Genetic Algorithm with a Piecewise Support Vector Machine (GA-PSVM) is developed that improves the precision of short term wind power...
The arithmetic of wind power prediction plays an important part in the development of wind power prediction. In this paper, based on the principles of support vector machine (SVM) and wavelet, the wavelet SVM model for short term wind power prediction is built up along with analyzing the characteristics of power curves of wind turbine generator systems. The operation data from a wind farm in North...
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