The Application of Simulated Annealing Particle Swarm Algorithm in the Short-term Wind Speed Prediction

DOI

10.2991/meici-16.2016.126How to use a DOI?

Keywords

Wind farm; Short-term wind speed forecasting; Simulation annealing-particle swarm optimization algorithm; BP neural network

Abstract

In view of the low prediction accuracy of short-term wind speed, a forecasting method based on simulation annealing particle swarm optimization BP neural network (SAPSO-BP) was proposed. The simulation results showed that the average absolute error and mean squared error of the proposed prediction model were better than several other optimization algorithms, and had better robustness, could be used for short-term wind forecasting.

Copyright

© 2016, 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  - Li Ai
AU  - Yan Xiong
PY  - 2016/09
DA  - 2016/09
TI  - The Application of Simulated Annealing Particle Swarm Algorithm in the Short-term Wind Speed Prediction
BT  - Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)
PB  - Atlantis Press
SP  - 604
EP  - 608
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-16.2016.126
DO  - 10.2991/meici-16.2016.126
ID  - Ai2016/09
ER  -