Short-term Load Forecasting Based On Geographic Information System

DOI

10.2991/mmebc-16.2016.151How to use a DOI?

Keywords

Short-term load forecasting,Genetic Annealing Algorithm,Support Vector Machine

Abstract

Short-term load forecasting is inevitable and important for electric power management.In order to predict precisely and reliably, the forecasting model is established combining Genetic Annealing Algorithm and Support Vector Machine, which respectively improve partly search capability and possesses more adaptability. Then both of them are gathered together to cooperate with Geographic Information System, which can perfectly combine graphic information and attribute data. Design relevant database and platform, then the practical forecasting method for short-term load is complete.

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  - Tong Li
PY  - 2016/06
DA  - 2016/06
TI  - Short-term Load Forecasting Based On Geographic Information System
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 714
EP  - 717
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.151
DO  - 10.2991/mmebc-16.2016.151
ID  - Li2016/06
ER  -