Wind power forecasting based on bagging extreme learning machine ensemble model

Abstract

The wind energy forecast is an useful tool for wind farm production planning, and operation, facilitating decision making in terms of maintenance, electricity market clearing, and load sharing. This study proposes a cooperative ensemble learning model, using time series preprocessing, multi-objective optimization, and artificial intelligence to forecast wind energy generation in two wind farms in Brazil. Multi-objective optimization is employed to combine variational mode decompositionbased components of a model with bootstrap aggregation (bagging) and extreme learning machine models. Forecasting accuracy is evaluated through the root mean squared error, mean absolute error, mean absolute percentage error, and Diebold-Mariano hypothesis test. The empirical results suggest that proposed ensemble learning model achieved better forecasting performance than bootstrap stacking, machine learning, artificial neural networks, and statistical models, with values of approximately 12.76%, 25.25%, 31.91%, and 34.76%, respectively, in terms of root mean squared errors reduction for out-of-sample forecasting.