A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique

As a dominant form of renewable energy sources with significant technical progress over the past decades, wind power is increasingly integrated into power grids. Wind is chaotic, random and irregular. For proper planning and operation of power systems with high wind power penetration, accurate wind...

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Autores principales:	Mehdi Abbasipour, Mosayeb Afshari Igder, Xiaodong Liang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Hybrid neural network machine learning day-ahead wind speed forecasting wind power Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8aac5c1715d44c1a9b2aeb58f21e3dcf
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id	oai:doaj.org-article:8aac5c1715d44c1a9b2aeb58f21e3dcf
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spelling	oai:doaj.org-article:8aac5c1715d44c1a9b2aeb58f21e3dcf2021-11-17T00:00:50ZA Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique2169-353610.1109/ACCESS.2021.3126747https://doaj.org/article/8aac5c1715d44c1a9b2aeb58f21e3dcf2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606927/https://doaj.org/toc/2169-3536As a dominant form of renewable energy sources with significant technical progress over the past decades, wind power is increasingly integrated into power grids. Wind is chaotic, random and irregular. For proper planning and operation of power systems with high wind power penetration, accurate wind speed forecasting is essential. In this paper, a novel hybrid Neural Network (NN)-based day-ahead (24 hour horizon) wind speed forecasting is proposed, where five hybrid neural network algorithms are evaluated. The five algorithms include Wavelet Neural Network (WNN) trained by Improved Clonal Selection Algorithm (ICSA), WNN trained by Particle Swarm Optimization (PSO), Extreme Learning Machine (ELM)-based neural network, Radial Basis Function (RBF) neural network, and Multi-Layer Perceptron (MLP) Neural Network. Single- and multi-features and their effect on the accuracy of wind speed prediction are also analyzed. The wind speed dataset used in this paper is Saskatchewan’s recorded historical wind speed data. Despite the excellent wind power potential, only 6.5% of the total electricity demand is currently supplied by wind power in Saskatchewan, Canada. This study paves the way for economical operation, planning, and optimization of Saskatchewan’s future wind power generation.Mehdi AbbasipourMosayeb Afshari IgderXiaodong LiangIEEEarticleHybrid neural networkmachine learningday-ahead wind speed forecastingwind powerElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151142-151154 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Hybrid neural network machine learning day-ahead wind speed forecasting wind power Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Hybrid neural network machine learning day-ahead wind speed forecasting wind power Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mehdi Abbasipour Mosayeb Afshari Igder Xiaodong Liang A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique
description	As a dominant form of renewable energy sources with significant technical progress over the past decades, wind power is increasingly integrated into power grids. Wind is chaotic, random and irregular. For proper planning and operation of power systems with high wind power penetration, accurate wind speed forecasting is essential. In this paper, a novel hybrid Neural Network (NN)-based day-ahead (24 hour horizon) wind speed forecasting is proposed, where five hybrid neural network algorithms are evaluated. The five algorithms include Wavelet Neural Network (WNN) trained by Improved Clonal Selection Algorithm (ICSA), WNN trained by Particle Swarm Optimization (PSO), Extreme Learning Machine (ELM)-based neural network, Radial Basis Function (RBF) neural network, and Multi-Layer Perceptron (MLP) Neural Network. Single- and multi-features and their effect on the accuracy of wind speed prediction are also analyzed. The wind speed dataset used in this paper is Saskatchewan’s recorded historical wind speed data. Despite the excellent wind power potential, only 6.5% of the total electricity demand is currently supplied by wind power in Saskatchewan, Canada. This study paves the way for economical operation, planning, and optimization of Saskatchewan’s future wind power generation.
format	article
author	Mehdi Abbasipour Mosayeb Afshari Igder Xiaodong Liang
author_facet	Mehdi Abbasipour Mosayeb Afshari Igder Xiaodong Liang
author_sort	Mehdi Abbasipour
title	A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique
title_short	A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique
title_full	A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique
title_fullStr	A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique
title_full_unstemmed	A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique
title_sort	novel hybrid neural network-based day-ahead wind speed forecasting technique
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8aac5c1715d44c1a9b2aeb58f21e3dcf
work_keys_str_mv	AT mehdiabbasipour anovelhybridneuralnetworkbaseddayaheadwindspeedforecastingtechnique AT mosayebafshariigder anovelhybridneuralnetworkbaseddayaheadwindspeedforecastingtechnique AT xiaodongliang anovelhybridneuralnetworkbaseddayaheadwindspeedforecastingtechnique AT mehdiabbasipour novelhybridneuralnetworkbaseddayaheadwindspeedforecastingtechnique AT mosayebafshariigder novelhybridneuralnetworkbaseddayaheadwindspeedforecastingtechnique AT xiaodongliang novelhybridneuralnetworkbaseddayaheadwindspeedforecastingtechnique
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A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique

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