A Self-Adaptive Kernel Extreme Learning Machine For Short-Term Wind Speed Forecasting

Wind speed forecasting with artificial neural networks (ANNs) plays important role in safely utilizing and integrating the wind power. With the rapid updated wind speed data, however, the only way to guarantee forecasting accuracy for these ANN models is re-training from scratch with an updated training dataset. Obviously, it is an inefficient work due to the resumption of constructing the new training dataset and re-training the model. To enhance training efficiency, reduce re-training cost and improve forecasting accuracy, a self-adaptive kernel extreme learning machine (KELM) is proposed in this paper. With an advanced and efficient learning process, the self-adaptive KELM could simultaneously obsolete old data and learn from new data by reserving overlapped information between the updated and old training datasets. To evaluate the efficiency and accuracy of the self adaptive KELM, the wind speed data from three different stations are employed as a numerical experiment. The Mean Absolute Error (MAE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) showed that the self-adaptive KELM with a simple structure could obtain more accurate forecasting results at a faster calculation speed than comparison models, where the proposed model decreased the MAPE values with 7.4776%, 3.5329% and 2.0900% in 1-step, 3-step and 5-step forecasting, respectively (C) 2020 Elsevier B.V. All rights reserved.