Research on Short-Term Load Forecasting Based on Improved Support Vector Regression

DOI

10.2991/iceeecs-16.2016.156How to use a DOI?

Keywords

artificial bee colony algorithm; support vector machine; micro grid; load forecasting

Abstract

Firstly, according to the disadvantages of the artificial bee colony algorithm which is easy to fall into the local optimum and the convergence rate is slow, this paper introduce the current optimal food source and inertia weight function to improve the food source updating method. Then according to the parameter optimization of support vector regression, this paper transform it into a combinatorial optimization problem, and use the improved artificial bee colony algorithm to solve the optimization problem, and then establish the prediction model of artificial bee colony algorithm to optimize the SVR. Taking the short-term load forecasting data of micro-grid as an example, the prediction results of the model are compared and analyzed. The results show that the model has the best forecasting effect and the shortest running time, and has better learning ability and generalization ability.

Copyright

© 2016, 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  - Baoyi Wang
AU  - Tianyang Han
AU  - Shaomin Zhang
PY  - 2016/12
DA  - 2016/12
TI  - Research on Short-Term Load Forecasting Based on Improved Support Vector Regression
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 794
EP  - 799
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.156
DO  - 10.2991/iceeecs-16.2016.156
ID  - Wang2016/12
ER  -