Load Forecasting by Considering Wind Power Based on Sequential Time Classification LSSVM Model

Chen Jun Yang; Ai Hui Zhang; Hai Wei Lu; Gang Wu; Hai Yan Ma; Xiao Feng Gao; Ming Qin

doi:10.4028/www.scientific.net/AMR.712-715.2437

Paper Titles

INS/GPS Integrated Navigation Method Based on Neural Network
p.2419

A Resilient Particle Swarm Optimization Algorithm with Dynamically Changing Inertia Weight
p.2423

Applications of Reservoir Sensitivity Prediction Based on Quantum Neural Networks
p.2428

Study on Application of Fast Intrinsic Mode Decomposition to Short-Term Load Forecasting
p.2432

Load Forecasting by Considering Wind Power Based on Sequential Time Classification LSSVM Model
p.2437

An Intelligent Algorithm Method of Element Layout Priority Sequence on Consoles Human Machine Interface
p.2441

A Hybrid Intelligence Algorithm for No-Wait Flow Shop Scheduling
p.2447

A User-Based Modeling and Analysis of Network Public Opinion Using Particle Swarm Optimization
p.2452

The Description and Characterization of Symmetric Frames and Gabor Frames and Applications in Material Engineering
p.2458

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 712-715Load Forecasting by Considering Wind Power Based...

Load Forecasting by Considering Wind Power Based on Sequential Time Classification LSSVM Model

Abstract:

In recent years, with the large-scale grid connection of wind power, wind power as an important factor to load forecasting should not be overlooked; A least squares-support vector machine (LSSVM) has been improved for the region including wind power, based on the influence from the load caused by the changes of wind and the characteristics between load and wind power. The method uses the models of least squares-support vector machine to classify and build different models , and gets the integration of each model for equivalent load forecasting, which provides the reference for the region including wind power.

You might also be interested in these eBooks

Advances in Manufacturing Science and Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 712-715)

Pages:

2437-2440

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.712-715.2437

Citation:

Cite this paper

Online since:

June 2013

Authors:

Chen Jun Yang, Ai Hui Zhang, Hai Wei Lu, Gang Wu, Hai Yan Ma, Xiao Feng Gao, Ming Qin

Keywords:

Anti-Peaking Characteristics, Equivalent Load, Least Squares Support Vector Machines (Ls-SVM), Load Forecasting, Sequential Time Classification, Wind Power

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Elattar,E.E ,Goulermas,J and Wu,Q.H. IEEE Transactions on,48(3): 438-447. (2010).

Google Scholar

[2] Han,X,S.Han,L.E,Gooi H.B. IET Generation, Transmission & Distribution.6(5)436-444(2012).

Google Scholar

[3] Zhiyong Ding and Ping Yang. Automation of Electric Power Systems, 36(14):131-136.(2012) In Chinese.

Google Scholar

[4] Gross,G. and Pacific N.V. Power Systems,IEEE Transactions on,3(2):368-374.(2012).

Google Scholar

[5] Shu Fan and Methaprayoon,K. Industry Applications,IEEE Transactions on, 45(4):1452-1459.

Google Scholar

[6] Quan Zhou, Wei Sun and Haijun Ren. Power System Technology, 66-71(2011), In Chinese.

Google Scholar