Proceedings of the 2015 International Conference on Test, Measurement and Computational Methods

Forecasting of Electricity Load Based on Improved Particle Swarm Optimization and Support Vector Regression Machine

Authors
Limei Liu
Corresponding Author
Limei Liu
Available Online November 2015.
DOI
https://doi.org/10.2991/tmcm-15.2015.32How to use a DOI?
Keywords
power system; support vector regression machine; forecasting of electricity load; improved particle swarm optimization
Abstract
Support vector regression machine is suitable for small sample decision and it is good to data forecasting capabilities. Its nature of learning method is under the condition of limited information to obtain a good ability in data mining. Accurate electricity load forecasting is an important practical value to our lives. This paper presents a new algorithm that is an improved particle swarm optimization algorithm and support vector regression machine that is proposed to predict electricity load. It is of great significance to forecasting electricity load. The algorithm can optimize training parameters of support vector regression machine by improved particle swarm optimization algorithm. The simulation experimental results indicate that the new algorithm made a meaningful exploration on forecasting electricity load.
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Proceedings
2015 International Conference on Test, Measurement and Computational Methods
Part of series
Advances in Computer Science Research
Publication Date
November 2015
ISBN
978-94-6252-132-2
ISSN
2352-538X
DOI
https://doi.org/10.2991/tmcm-15.2015.32How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Limei Liu
PY  - 2015/11
DA  - 2015/11
TI  - Forecasting of Electricity Load Based on Improved Particle Swarm Optimization and Support Vector Regression Machine
BT  - 2015 International Conference on Test, Measurement and Computational Methods
PB  - Atlantis Press
SP  - 130
EP  - 133
SN  - 2352-538X
UR  - https://doi.org/10.2991/tmcm-15.2015.32
DO  - https://doi.org/10.2991/tmcm-15.2015.32
ID  - Liu2015/11
ER  -