Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil

Abstract

Machine learning algorithms (MLAs) are applied to predict wind speed data for Osorio wind farm that is located in the south of Brazil, near the Osorio city. Forecasting wind speed in wind farm regions is valuable in order to obtain an intelligent management of the generated power and to promote the utilization of wind energy in grid-connected and isolated power systems. In this study, multilayer feed-forward neural network (MLFFNN), support vector regression (SVR), fuzzy inference system (FIS), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH) type neural network, ANFIS optimized with particle swarm optimization algorithm (ANFIS-PSO) and ANFIS optimized with genetic algorithm (ANFIS-GA) are developed to predict the time-series wind speed data. The Time-series prediction describes a model that predicts the future values of the system only using the past values. Past data is entered as input and future data to be used for represents MLA output. The developed models are examined on 5-min, 10-min, 15-min and 30-min intervals of wind speed data. The results demonstrated that the GMDH model for all time intervals can successfully predict the time-series wind speed data with a high accuracy. Also, the combination of ANFIS models with PSO and GA algorithms can increase the prediction accuracy of the ANFIS model for all time intervals.