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Title: A learning-based short-term wind speed forecasting approach through spiking neural networks

Authors: Jing Hu; Lili Xie; Xinyi Chen; Weidong Liu; Xingpeng Zhang; Dianwei Qian

Addresses: Shanghai Minghua Electric Power Science & Technology Co., Ltd., Shanghai 200090, China ' Shanghai Electric Power New Energy Development Co., Ltd., Shanghai 200010, China ' Shanghai Electric Power New Energy Development Co., Ltd., Shanghai 200010, China ' Shanghai Electric Power New Energy Development Co., Ltd., Shanghai 200010, China ' Shanghai Electric Power New Energy Development Co., Ltd., Shanghai 200010, China ' School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing 102206, China

Abstract: In real-world applications, the index of wind speed is concerned to many fields. This index plays an extremely important role in wind power systems. Unfortunately, it is hard enough to accurately measure the wind speed. Its forecasting undoubtedly becomes harder and more challenging. This paper focuses on the problem of short-term wind speed forecasting. It is too complex to model the wind speed by mathematical formulas. The technique of neural networks is a learning-based approach. By this technique, the method of spiking neural networks is one of the most successful methods to fulfil the modelling of complex dynamics and the exploitation of learning ability. This paper investigates a spiking-neural-network-based structure, designs a hybrid learning algorithm that combines the adaptive learning rate and the momentum term and implements them for the short-term wind speed forecasting. Experiments and comparisons are illustrated to show the effectiveness and feasibility of this learning-based forecasting approach.

Keywords: wind speed; forecasting; short-term; spiking neural networks; SNNs; learning algorithm; modelling; power generation.

DOI: 10.1504/IJAMECHS.2020.109907

International Journal of Advanced Mechatronic Systems, 2020 Vol.8 No.1, pp.26 - 35

Received: 26 Nov 2019
Accepted: 03 Feb 2020

Published online: 15 Sep 2020 *

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