Abstract
Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF) is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.
Highlights
Wind power is a clean, renewable form of energy that can be developed and utilized relatively ; it has garnered increased attention
EMAPE, the mean absolute error (MAE) evaluation index decreases by approximately 40% (EMAE), and the root mean squared error (RMSE) ERMSE are used to evaluate the forecast obtained from the model
Method for selecting input variables, the wavelet transform (WT)-random forests (RF)-kernel-based extreme learning machine (KELM)-GA model is compared with persistence model, radial basis function (RBF), neural networks (NN), support vector machines (SVM) and extreme learning machine (ELM)
Summary
Wind power is a clean, renewable form of energy that can be developed and utilized relatively ; it has garnered increased attention. Partial autocorrelation function [4,8], phase space reconstruction [10], granger causality test [8], coral reefs optimization [12] and other methods were validated successfully in the input selection Most of these methods place emphasis on the analysis between the candidate variables, instead of on the variables and the model performance. An input variable selection method based on RF that improves wind speed forecasting accuracy is proposed. A short-term wind speed forecasting model is constructed using the selected optimal feature set as the set of input variables for the KELM. The RF feature selection method identifies the fewest features to represent the original information, simplifies the structure of the wind speed forecasting model, reduces the training time, and improves the model’s accuracy and generalization ability, all of which demonstrate that the input variables selected using the RF method are effective.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.