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

Xiaonan Zhou, Ye Tang, Yulei Xie, Yalou Li, Hongliang Zhang

China Electric Power Research Institute, Beijing, China.
Electric Power Research Institute of Guangdong Grid Corporation, Guangzhou, China.
Key Laboratory of Regional Energy System Optimization，Ministry of Education, North China Electric Power University, Beijing, China.

DOI: 10.4236/epe.2013.54B094                

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.

Keywords

Fuzzy Probability; Markov Chain Model; Power Load Prediction; Satisfaction Degree; Uncertainty

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Y. Cui, et al, “Application of Grey-Markov Prediction Model on Long-Term Load Forecasting,” Modern Electric Power, Vol. 3, No. 28, 2011,pp. 38-47.
[2]	C. C. Hsu and C. Y. Chen, “Regional Load Forecasting in Taiwan-Applications of Artificial Neural Networks,” Energy Conversion Management, Vol. 44, No. 12, 2003, pp. 1941-1949. doi:10.1016/S0196-8904(02)00225-X
[3]	P. F. Pai and W. C. Hong, “Forecasting Regional Electricity Load Based on Recurrent Support Vector Machines with Genetic Algorithms,” Electric Power Systems Research, Vol. 74, No. 3, 2005, pp. 417-425. doi:10.1016/j.epsr.2005.01.006
[4]	P. F. Pai, “Hybrid Ellipsoidal Fuzzy System in Forecasting Regional Electricity Loads,” Energy Conversion Management, 2006, Vol. 47, No. 15-16, pp. 2283-2289. doi:10.1016/j.enconman.2005.11.017
[5]	L.-C. Ying and M.-C. Pan, “Using Adaptive Network Based Fuzzy Inference System to Forecast Regional Electric Loads,” Energy Conversion and Management, Vol. 49, 2008, pp. 205-211.
[6]	N. Ye, “A Markov Chain Model of Temporal Behavior for Anomaly Detection,” Workshop on Information Assurance and Security, 2000.
[7]	T. M. Mithchel, Machine Learning, Boston, Ma: McGraw-Hill, 1997.