Multistep Forecasting for Short-Term Wind Speed Using an Optimized Extreme Learning Machine Network with Decomposition-Based Signal Filtering

Zhao, J; Wang, J; Liu, F

Multistep Forecasting for Short-Term Wind Speed Using an Optimized Extreme Learning Machine Network with Decomposition-Based Signal Filtering

Zhao, J Wang, J Liu, F

Permalink

Publisher:: ASCE-AMER SOC CIVIL ENGINEERS
Publication Type:: Journal Article
Citation:: Journal of Energy Engineering, 2016, 142, (3)
Issue Date:: 2016-09-01

Closed Access

	Filename	Description	Size
	(ASCE)EY.1943-7897.0000291.pdf	Published version	39.74 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhao, J
dc.contributor.author	Wang, J
dc.contributor.author	Liu, F https://orcid.org/0000-0002-5005-9129
dc.date.accessioned	2022-08-13T21:26:51Z
dc.date.available	2022-08-13T21:26:51Z
dc.date.issued	2016-09-01
dc.identifier.citation	Journal of Energy Engineering, 2016, 142, (3)
dc.identifier.issn	0733-9402
dc.identifier.issn	1943-7897
dc.identifier.uri	http://hdl.handle.net/10453/160098
dc.description.abstract	Since wind fluctuates with strong variation even within a short-term period, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to solve. This paper contributes to multistep forecasting for short-term wind speed by developing a three-stage hybrid approach named MECE; it is a combination of the ensemble empirical model decomposition (EEMD) method, cuckoo search (CS) algorithm, and extreme learning machine (ELM) method. As the first stage of the hybrid MECE approach, a signal filtering based on a decomposition and reconstruction strategy is adopted and copied by the EEMD method, and a denoised series can be obtained. Then, the CS-optimized ELM is designed as a novel learning method to construct a single layer feed-forward neural network (SLFN); the input weights and biases are determined by the CS algorithm instead of the random initialization within the original ELM. Next, a training and forecasting stage is taken; three different strategies are adopted for multistep forecasting. The chosen data sets are half-hour wind speed observations, including 16 samples, and the simulation indicates that the proposed MECE approach performs much better than the traditional ones when addressing short-term wind speed forecasting problems.
dc.language	English
dc.publisher	ASCE-AMER SOC CIVIL ENGINEERS
dc.relation.ispartof	Journal of Energy Engineering
dc.relation.isbasedon	10.1061/(ASCE)EY.1943-7897.0000291
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.title	Multistep Forecasting for Short-Term Wind Speed Using an Optimized Extreme Learning Machine Network with Decomposition-Based Signal Filtering
dc.type	Journal Article
utslib.citation.volume	142
utslib.for	0905 Civil Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-13T21:26:28Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	142
utslib.citation.issue	3

Abstract:

Since wind fluctuates with strong variation even within a short-term period, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to solve. This paper contributes to multistep forecasting for short-term wind speed by developing a three-stage hybrid approach named MECE; it is a combination of the ensemble empirical model decomposition (EEMD) method, cuckoo search (CS) algorithm, and extreme learning machine (ELM) method. As the first stage of the hybrid MECE approach, a signal filtering based on a decomposition and reconstruction strategy is adopted and copied by the EEMD method, and a denoised series can be obtained. Then, the CS-optimized ELM is designed as a novel learning method to construct a single layer feed-forward neural network (SLFN); the input weights and biases are determined by the CS algorithm instead of the random initialization within the original ELM. Next, a training and forecasting stage is taken; three different strategies are adopted for multistep forecasting. The chosen data sets are half-hour wind speed observations, including 16 samples, and the simulation indicates that the proposed MECE approach performs much better than the traditional ones when addressing short-term wind speed forecasting problems.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/160098