IEEE Computational Intelligence Magazine - August 2021 - 50
Forecasting Wind Speed
Time Series Via Dendritic
Neural Regression
Junkai Ji, Minhui Dong, and Qiuzhen Lin
Shenzhen University, CHINA
Kay Chen Tan
The Hong Kong Polytechnic University, HONG KONG SAR.
Abstract-Wind energy is considered one of the fastest
growing renewable ('green') energy resources. Precise wind
power forecasting is imperative to ensure reliable power system
planning and wind farm operation. However, traditional
methods cannot yield satisfactory forecasts because of the
chaotic properties and high volatility of wind speed time
Digital Object Identifier 10.1109/MCI.2021.3084416
Date of current version: 15 July 2021
50 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2021
series. To address this issue, the use of artificial neural networks
has attracted increasing attention owing to their significantly
enhanced prediction accuracy. Based on these
considerations, a novel neural model with a dynamic dendrite
structure, known as the dendritic neuron model
(DNM), can be adopted for wind speed time series prediction.
The DNM is a plausible biological neural model that
was originally designed for classification problems; accordingly,
this study proposes the use of a regressive version of
the DNM, named dendritic neural regression (DNR), in
which the dendrite strength of each branch is considered. To
enhance the prediction performance, the recently proposed
states of matter search (SMS) optimization algorithm is used
to optimize the neural architecture for DNR. By virtue of
the powerful search ability of the SMS algorithm, DNR can
Corresponding Author: Qiuzhen Lin (e-mail: qiuzhlin@szu.edu.cn).
1556-603X/21©2021IEEE
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IEEE Computational Intelligence Magazine - August 2021
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