Title :
The Monthly Electricity Load Forecast Based on Composite Model
Author :
Xu Qifeng;Wang Qiang;Yao Zhilin;Liu Shufen
Author_Institution :
Jilin Univ., ChangChun, China
Abstract :
In the electric power area, Electric Power Load Forecasting (EPLF) is a fundamental process in the planning of monthly electricity production of electric power systems. In this paper, we discuss different kind of consumptions of various consumer groups. We apply Smooth Processing on the case without significant fluctuation after doing Wavelet Transform, then we predict with AR model, and get EPLF from the combination of electricity load of various cases. The method that we proposed is verified by history data, the result shows that it can archeieve accurate EPLF.
Keywords :
"Predictive models","Load modeling","Wavelet transforms","Forecasting","Data models","Power systems"
Conference_Titel :
Information Technology in Medicine and Education (ITME), 2015 7th International Conference on
DOI :
10.1109/ITME.2015.57