Decomposition-based short-term wind power forecasting for isolated power systems

Wind energy penetration has increased significantly and is playing a crucial role in the conversion of power systems to renewable energy. Remote and isolated power systems are increasing wind generation due to high cost of diesel fuel and transportation. To address the concerns of system frequency and scheduling from high penetration of stochastic wind generation, accurate short-term wind power forecasting is required. The research Investigates temporal resolution of wind energy data to improve neural network based forecast models. High resolution wind power data is used to simulate different temporal resolution, for both 10 minute and 1 hour forecast horizons. Three decomposition methods are compared wavelet, empirical mode, and variable mode decomposition. They each decomposed the sampled data into different modes, firstly a long-term component of lower frequencies, then more modes with detailed higher frequency components. To evaluate the temporal resolution and decomposition methods Back propagation neural network (BP), long short-term memory neural network (LSTM) and a convolutional neural network (CNN) are evaluated using wind power data from the King Island power system