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With the increasing contribution of the power production by the photovoltaic (PV) systems to the electricity supply, the PV power forecasting becomes increasingly important. There are many factors influencing the forecasting performance, such as the air temperature, humidity, insolation, wind speed, wind direction and so on. This study proposes a Takagi–Sugeno (T–S) fuzzy model-based PV power short-term...
This paper proposes a wind power forecasting model based on the empirical mode decomposition (EMD) and the support vector machine (SVM). In this model, the EMD is used to decompose wind power sequence into several intrinsic mode functions (IMF) and a residual component. Then, the SVM is used to train each component for the optimal parameters and kernel function. Finally, sum the prediction results...
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