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

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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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  -