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Wind power forecasting for the Villonaco wind farm

Abstract:

Wind energy is a non-programmable form of generation, hence, accurate and reliable wind energy pbkp_rediction is of great importance for the efficient operation of wind farms. This article presents a study for the pbkp_rediction of active power for the Villonaco Wind Farm (VWF), located in southern Ecuador at approximately 2700 m above sea level. Through the use of artificial neural networks, experimental tests are developed based on the models of Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) to obtain a hybrid model that fits the best characteristics of the individual models. Data from the active power SCADA (Supervisory Control and Data Acquisition) system for the years 2014 to 2018 are used to train and validate the models. Hybrid model is presented as the most appropriate option by the values obtained, viz., the mean absolute error (MAE), the mean squared error (MSE), and mean absolute percentage error (MAPE) that were 0.1365, 0.0974, and 144.26, respectively, outperforming to the others wind power forecast models.