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A Fuzzy Probability-based Markov Chain Model for Electric Power Demand Forecasting of Beijing, China

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DOI: 10.4236/epe.2013.54B094    2,816 Downloads   3,655 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

X. Zhou, Y. Tang, Y. Xie, Y. Li and H. Zhang, "A Fuzzy Probability-based Markov Chain Model for Electric Power Demand Forecasting of Beijing, China," Energy and Power Engineering, Vol. 5 No. 4B, 2013, pp. 488-492. doi: 10.4236/epe.2013.54B094.

References

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