Research on the Remaining Load Forecasting of Micro-Gird based on Improved Online Sequential Extreme Learning Machine

DOI

10.2991/icmmita-15.2015.108How to use a DOI?

Keywords

ELM; Remaining Load Forecasting; Online Sequential; Micro-Grid

Abstract

In order to improve the forecast accuracy and stability of the micro-grid uncontrollable remaining load and provide a more reliable basis for micro-grid power generation plan, an ultra-short-term micro-grid uncontrollable remaining load forecasting model based on the improved online sequential Extreme Learning Machine is proposed. Aimed at the wind and solar power generation and load characteristics, the weight update of old and new training data is added to the Extreme Learning Machine. And the average value of multi-module is used to enhance the predict stability of the algorithm. After the real data from UCI Machine Learning Repository is analyzed, the result shows that the algorithm is superior to the traditional Extreme Learning Machine (ELM) and the online sequential Extreme Learning Machine (OS-ELM) and the proposed algorithm is feasible.

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  - Shaomin Zhang
AU  - Peng Zhou
AU  - Baoyi Wang
PY  - 2015/11
DA  - 2015/11
TI  - Research on the Remaining Load Forecasting of Micro-Gird based on Improved Online Sequential Extreme Learning Machine
BT  - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 562
EP  - 567
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-15.2015.108
DO  - 10.2991/icmmita-15.2015.108
ID  - Zhang2015/11
ER  -