Research on the Remaining Load Forecasting of Micro-Gird based on Improved Online Sequential Extreme Learning Machine
- https://doi.org/10.2991/icmmita-15.2015.108How to use a DOI?
- ELM; Remaining Load Forecasting; Online Sequential; Micro-Grid
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/icmmita-15.2015.108 ID - Zhang2015/11 ER -