Short-term wind speed forecasting using support vector regression optimized by cuckoo optimization algorithm
type: Item
instance of: scholarly article
title: Short-term wind speed forecasting using support vector regression optimized by cuckoo optimization algorithm
zbMATH Open document ID: 1394.86016
DOI: 10.1155/2015/619178
author: Jianzhou Wang
author: Haiyan Jiang
author: Ru Hou
author: Qingping Zhou
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP) and optimized support vector regression (SVR). Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA), particle swarm optimization algorithm (PSO), and cuckoo optimization algorithm (COA). Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1) analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2) the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3) the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.
Mathematics Subject Classification ID: 86A32
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 6926814
Wikidata QID: Q59119135
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/619178
OpenAlex ID: W2146314856
cites work: Support-vector networks
cites work: Q4692508
cites work: An optimized forecasting approach based on grey theory and cuckoo search algorithm: a case study for electricity consumption in New South Wales
cites work: Pathological tremors: Deterministic chaos or nonlinear stochastic oscillators?
description: scientific article; zbMATH DE number 6926814