Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)

Short-Term Wind Power Forecasting Based on Spatiotemporal Correlations

Authors
Jinli Dou, Chun Liu, Bo Wang
Corresponding Author
Jinli Dou
Available Online May 2018.
DOI
https://doi.org/10.2991/meees-18.2018.3How to use a DOI?
Keywords
Short-Term wind power forecasting, Deep Neural Network, Convolutional Neural Network, spatiotemporal correlations.
Abstract

Wind power forecasting is of great significance for promoting the new energy resources accommodation and the economic efficiency, security and stability of power grid operation. The traditional wind power forecasting model has monotonous elements for input and demonstrates a lack of spatiotemporal correlation between elements. Therefore, a deep neural network model considering space-time correlation is proposed. Firstly, a multi-dimensional space-time data input modeling method is proposed based on meshed numerical weather forecasting. Then, a variety of deep neural network models for wind power prediction are established. Multi-layer CNN (Convolutional Neural Networks) are utilized for feature extraction, meanwhile LSTM (Long Short-Term Memory) networks are used for pattern memory. Finally, the prediction of single station and regional wind power is carried out respectively, which proves the effectiveness and feasibility of the method. The results show that, compared with the traditional single-layer neural network, the deep neural network in this paper can effectively mine the spatiotemporal correlation between data and improve the prediction accuracy of single-station wind power forecasting.

Copyright
© 2018, 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 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)
Series
Advances in Engineering Research
Publication Date
May 2018
ISBN
978-94-6252-534-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/meees-18.2018.3How to use a DOI?
Copyright
© 2018, 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  - Jinli Dou
AU  - Chun Liu
AU  - Bo Wang
PY  - 2018/05
DA  - 2018/05
TI  - Short-Term Wind Power Forecasting Based on Spatiotemporal Correlations
BT  - Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)
PB  - Atlantis Press
SP  - 12
EP  - 15
SN  - 2352-5401
UR  - https://doi.org/10.2991/meees-18.2018.3
DO  - https://doi.org/10.2991/meees-18.2018.3
ID  - Dou2018/05
ER  -