TITLE:
A Fuzzy Probability-based Markov Chain Model for Electric Power Demand Forecasting of Beijing, China
AUTHORS:
Xiaonan Zhou, Ye Tang, Yulei Xie, Yalou Li, Hongliang Zhang
KEYWORDS:
Fuzzy Probability; Markov Chain Model; Power Load Prediction; Satisfaction Degree; Uncertainty
JOURNAL NAME:
Energy and Power Engineering,
Vol.5 No.4B,
October
29,
2013
ABSTRACT:
In this study, a fuzzy probability-based Markov chain
model is developed for forecasting regional long-term electric power demand.
The model can deal with the uncertainties in electric power system and reflect
the vague and ambiguous during the process of power load forecasting through
allowing uncertainties expressed as fuzzy parameters and discrete intervals.
The developed model is applied to predict the electric power demand of Beijing
from 2011 to 2019. Different satisfaction degrees of fuzzy parameters are
considered as different levels of detail of the statistic data. The results indicate
that the model can reflect the high uncertainty of long term power demand,
which could support the programming and management of power system. The fuzzy
probability Markov chain model is helpful for regional electricity power system
managers in not only predicting a long term power load under uncertainty but
also providing a basis for making multi-scenarios power generation/development
plans.