Proceedings of the 3rd International Conference on Advances in Energy and Environmental Science 2015

Wind Power Real-time Prediction Based on Improved Time Series and Grey Model

Authors
Lanlan Chen, Zixia Pei, Anjia Mao, Yan Liu, Zhuohang Wu
Corresponding Author
Lanlan Chen
Available Online July 2015.
DOI
https://doi.org/10.2991/icaees-15.2015.81How to use a DOI?
Keywords
wind power, time series, grey model, combination model
Abstract

This paper aims to establish a suitable wind power forecasting model used to forecast the power of the wind farm. An improved real-time series model is built by linear function and Fourier function. For raw data, the picture of historical data is adopted to correct them. In order to improve the prediction accuracy of wind power, it proposes the linear combination model based on improved time series model and grey model. The model uses a fixed weight method. Mathematical analysis and calculation results show that combination model, which has certain reference value, is simple and can improve the prediction accuracy overall.

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 3rd International Conference on Advances in Energy and Environmental Science 2015
Series
Advances in Engineering Research
Publication Date
July 2015
ISBN
978-94-6252-130-8
ISSN
2352-5401
DOI
https://doi.org/10.2991/icaees-15.2015.81How 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  - Lanlan Chen
AU  - Zixia Pei
AU  - Anjia Mao
AU  - Yan Liu
AU  - Zhuohang Wu
PY  - 2015/07
DA  - 2015/07
TI  - Wind Power Real-time Prediction Based on Improved Time Series and Grey Model
BT  - Proceedings of the 3rd International Conference on Advances in Energy and Environmental Science 2015
PB  - Atlantis Press
SP  - 433
EP  - 438
SN  - 2352-5401
UR  - https://doi.org/10.2991/icaees-15.2015.81
DO  - https://doi.org/10.2991/icaees-15.2015.81
ID  - Chen2015/07
ER  -