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An Optimized Deep Nonlinear Integrated Framework for Wind Speed Forecasting and Uncertainty Analysis by Jujie Wang; Dongming Gao; Zhenzhen Zhuang is a Computer Science article available to read on EtoBox.

What is An Optimized Deep Nonlinear Integrated Framework for Wind Speed Forecasting and Uncertainty Analysis about?

Accurate wind speed prediction improves the efficiency of electricity and also increases the economic benefits of wind farms. However, the previous studies do not fully consider the chronological feature of wind speed, and do not properly identify and use the characteristic decomposed components of wind speed series in different time periods. The phenomenon of insufficient and excessive decomposition of components weakens the prediction accuracy of the model and destroys the power generation plan of the wind farm. In this paper, a segmented multi-modal deep learning integrated model based on periodicity is proposed. Firstly, the time series is reconstructed into matrix according to date; the matrix is divided into different segment matrices according to chronological characteristics; each sub-matrix is spliced into sub-sequence according to chronological order. Secondly, the contribution extremum method is used to determine the optimal feature components of all sub-sequences. Thirdly, the optimized deep learning model integrates all sub-sequences and then obtains the prediction results of the current date. Finally, the uncertainty analysis of the predicted value further improves th

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Author

Jujie Wang; Dongming Gao; Zhenzhen Zhuang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)