The Application of Joint Optimization Method using GA for Load Forecasting

DOI

10.2991/icmmcce-15.2015.470How to use a DOI?

Keywords

Least square support vector machine (LSSVM); genetic algorithm (GA); parameter optimization; phase space; short-term load forecasting.

Abstract

Since load forecasting plays an important role in the planning and operation of power industry, substantial efforts are made in improving the accuracy and reliability of load forecasting. In this paper, we develop a novel hybrid approach based on phase space reconstruction and least square support vector for the short-term load forecasting. However, the proper parameters in phase space reconstruction and least square vector machine have a significant effect on the forecasting performance, and there is no standard solution for the parameter estimation problem. Therefore, in this paper, the genetic algorithm (GA) approach is employed to optimize the parameters of both phase space reconstruction and least square support vector machine together. The experimental results suggest that the joint optimization parameter is superior to the separate optimization solutions.

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  - Junsong Qin
AU  - Dongxiao Niu
AU  - Jinpeng Qiu
AU  - Ling Ji
PY  - 2015/12
DA  - 2015/12
TI  - The Application of Joint Optimization Method using GA for Load Forecasting
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SP  - 899
EP  - 906
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.470
DO  - 10.2991/icmmcce-15.2015.470
ID  - Qin2015/12
ER  -