Research on the Remaining Load Forecasting of Micro-Gird based on Improved Online Sequential Extreme Learning Machine
Shaomin Zhang, Peng Zhou, Baoyi Wang · Advances in computer science research · 2015
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.