Open AccessPeak Load Forecasting for Electrical Bus based on Limited Historical Data under Complex Meteorological Condition
Author(s) -
Heyan Zhu,
Changyong Yu,
Qingkui He,
Song Kun,
Jingbo Liu,
Yan-Lu Xu,
Tianqi Lu
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/440/3/032121
Subject(s) - electrical load , particle swarm optimization , redundancy (engineering) , feature selection , computer science , ranking (information retrieval) , feature (linguistics) , extreme learning machine , data mining , artificial intelligence , engineering , machine learning , artificial neural network , voltage , electrical engineering , linguistics , philosophy , operating system
The historical data of peak load for electrical bus are limited and fluctuated violently. The fluctuation ability of peak load for electrical bus is nonlinear and randomly. So its prediction accuracy is low. In order to improve the accuracy of peak load forecasting for electrical bus, a peak load forecasting for electrical bus method based on limited historical data under complex weather conditions is proposed. Firstly, the influence of natural meteorology, society and other factors on peak load fluctuation for electrical bus is analysed; Secondly, based on the reduction of redundancy between features of potential feature set, the feature importance ranking is obtained by conditional mutual information (CMI). Then, according to the improved particle swarm optimization extreme learning machine suitable for small sample training, the forward feature selection is performed to determine the optimal feature subset. Finally, based on the optimal feature subset, the optimal peak load forecasting model is established.