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

DOI: 10.4236/epe.2013.54B094   PDF   HTML     4,559 Downloads   5,481 Views   Citations


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.

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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.

Conflicts of Interest

The authors declare no conflicts of interest.


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