Proceedings of the 2015 International Conference on Power Electronics and Energy Engineering

Research of wind power prediction based on the auto-regressive model

Authors
L. Feng, C.H. Liang, H. Huang
Corresponding Author
L. Feng
Available Online April 2015.
DOI
10.2991/peee-15.2015.16How to use a DOI?
Keywords
Wind power prediction, Time series analysis, Self-regressive mathematical model, Simulation
Abstract

This paper discusses in detail the reason for the inaccurate result from the present system for wind farm output power prediction. Time series analysis method was applied for improving existing problem in prediction model and treatment method of basic data. Improving self-regressive mathematical model was established and taken the model identification. Using SPSS software simulates and further assists model identification and using given wind farm historical output power data to forecast one and multi-wind power unit output power in odd-number days and a week. Finally, this paper compares and analyses the getting prediction power and expound the next step work that improves the wind power prediction accuracy.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Power Electronics and Energy Engineering
Series
Advances in Engineering Research
Publication Date
April 2015
ISBN
978-94-62520-83-7
ISSN
2352-5401
DOI
10.2991/peee-15.2015.16How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - L. Feng
AU  - C.H. Liang
AU  - H. Huang
PY  - 2015/04
DA  - 2015/04
TI  - Research of wind power prediction based on the auto-regressive model
BT  - Proceedings of the 2015 International Conference on Power Electronics and Energy Engineering
PB  - Atlantis Press
SP  - 61
EP  - 64
SN  - 2352-5401
UR  - https://doi.org/10.2991/peee-15.2015.16
DO  - 10.2991/peee-15.2015.16
ID  - Feng2015/04
ER  -