Multi-Step Wind Speed Forecasting Using Signal Decomposing Algorithms, Bat Optimization Algorithm and Least Squares Support Vector Machine

DOI

10.2991/ncce-18.2018.19How to use a DOI?

Keywords

non-stationary; wind speed; four signal decomposing algorithms.; bat algorithm; decomposing algorithm.

Abstract

Accurate forecasting of short term wind speed has been widely applied in the disaster early warning of civil engineering. Considering the characteristics of non-stationary and nonlinear of wind speed, the actual wind speed time series need to be decomposed first and then predicted. In this paper, a set of actual wind speed time series of typhoon is decomposed by four signal decomposing algorithms. (e.g., Wavelet Packet Decomposition/Ensemble Empirical Mode Decomposition/Variational Mode Decomposition/Empirical Wavelet Transform) And the features of intrinsic mode functions from these four methods are fully evaluated. Finally, multi-step wind speed forecasting experiment based on least squares support vector machine optimized by bat algorithm is carried out to testify the effectiveness of the signal decomposing algorithm. The results of experiences indicate that the Empirical Wavelet Transform is effective in the wind speed accurate forecasting

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhou Li
AU  - Chunxiang Li
PY  - 2018/05
DA  - 2018/05
TI  - Multi-Step Wind Speed Forecasting Using Signal Decomposing Algorithms, Bat Optimization Algorithm and Least Squares Support Vector Machine
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 110
EP  - 115
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.19
DO  - 10.2991/ncce-18.2018.19
ID  - Li2018/05
ER  -