Proceedings of the International Conference on Advances in Energy, Environment and Chemical Engineering

Load Forecasting of Power System Based on Integrated Sample System and Cloud Computing

Authors
Huizhong Wang, Ke Liu, Hongyi Zhu
Corresponding Author
Huizhong Wang
Available Online September 2015.
DOI
10.2991/aeece-15.2015.32How to use a DOI?
Keywords
Integrated Sample System. Cloud computing. Particle swarm optimization. LSSVM.
Abstract

According to the characteristics of the short-term load forecasting, this paper established a integrated sample system. Through the analysis of various factors and load data to evaluate the effects of various factors on load forecasting, choosing the most appropriate forecast samples. PSO-LSSVM-Cloud model is established using Cloud computing technology to improve the efficiency of prediction. Finally, the actual data to establish PSO-LSSVM-Cloud model simulation comparison. Experimental results show that this load forecasting method has high forecasting precision and efficiency.

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 International Conference on Advances in Energy, Environment and Chemical Engineering
Series
Advances in Engineering Research
Publication Date
September 2015
ISBN
10.2991/aeece-15.2015.32
ISSN
2352-5401
DOI
10.2991/aeece-15.2015.32How 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  - Huizhong Wang
AU  - Ke Liu
AU  - Hongyi Zhu
PY  - 2015/09
DA  - 2015/09
TI  - Load Forecasting of Power System Based on Integrated Sample System and Cloud Computing
BT  - Proceedings of the International Conference on Advances in Energy, Environment and Chemical Engineering
PB  - Atlantis Press
SP  - 156
EP  - 160
SN  - 2352-5401
UR  - https://doi.org/10.2991/aeece-15.2015.32
DO  - 10.2991/aeece-15.2015.32
ID  - Wang2015/09
ER  -