Welcome To WUJNS
武汉大学学报 英文版 | Wuhan University Journal of Natural Sciences
Wan Fang
Wuhan University
Latest Article
Prediction of End-Use Energy Consumption in a Region of Northwest China
YANG Xing1,2, KANG Hui1, NIU Dongxiao1†
1. School of Economics and Management, North China Electric Power University, Beijing 102206, China; 2. State Grid Qinghai Electric Power Company, Xining 810008, Qinghai, China
 End-use energy consumption can reflect the industrial development of a country and the living standards of its residents. The study of end-use energy consumption can provide a solid basis for industrial restructuring, energy saving, and emission reduction. In this paper, we analyzed the end-use energy consumption of a region in Northwestern China, and applied the Markov prediction method to forecast the future demand of different types of end-use energy. This provides a reference for the energy structure optimization in the Northwestern China.
Key words: end-use energy consumption; Markov model; transition probability matrix; energy consumption forecast
CLC number:TK 01
[1]	Wang G, Xu Y, Ye C J. Analysis of the terminal energy structure evolution process in Zhejiang province [J]. Energy Engineering, 2015, (1): 26-30(Ch).
[2]	Chen T T. Research on Electricity Consumption Develop-ment Forecasting in Final Energy Consumption of Shanxi[D]. Beijing: North China Electric Power University, 2015(Ch).
[3]	Wan J, Zhou X B, Du Z. Correlation analysis and prediction of final energy consumption and economic growth in Hubei province[J]. Hubei Electric Power, 2015, (2):71-74(Ch).
[4]	Liu Z Y. Electric Power and Energy in China [M]. Beijing: China Electric Power Press, 2012(Ch).
[5]	Zhang H, Wang F L, Suo R X. Forecasting the industry structure of the east, the middle and the west based on Markov model [J]. Mathematics in Practice and Theory, 2010, 40(7): 39-44(Ch).
[6]	Tang X W, Zeng Y. The estimation of transition probability of Markov chains in market forecasting [J]. Journal of UEST of China, 1994, (12): 643-648(Ch).
[7]	Vassiliou P C G, Vasileiou A. Asymptotic behaviour of the survival probabilities in an inhomogeneous semi-Markov model for the migration process in credit risk[J]. Linear Al-gebra and Its Applications, 2013, 438(7): 2880-2903.
[8]	Zhou Y, Li Z. The application of Markov skeleton process on the infectious disease management model [C] // International Conference on Artificial Intelligence, Management Science and Electronic Commerce. Washington D C: IEEE, 2011: 2477-2480.
[9]	Zhou Y, Fan H. The application of Markov skeleton process on the population forecast [J]. Journal of Convergence    Information Technology, 2012, 7(19): 373-380(Ch).
[10]	Zhang H, Zhang W, Palazoglu A, et al. Prediction of ozone levels using a hidden Markov model (HMM) with Gamma distribution [J]. Atmospheric Environment, 2012, 62(15): 64- 73(Ch).
[11]	He Y, Pu Y, Wang J, et al. Spatial-temporal dynamics of Sichuan industrial structure with Markov chains approach [J]. The International Conference on Geoinformatics, 2010, 2(1): 1-6.
[12]	Leithon J, Lim T J, et al. Renewable energy management in cellular networks: An online strategy based on ARIMA forecasting and a Markov chain model [C] // Wireless Communications and Networking Conference. Washington D C: IEEE, 2016: 1-6.
[13]	Xie H, Yang L, Wang J, et al. Short-term power forecasting for photovoltaic generation based on wavelet neural net-work and residual correction of Markov chain [C] // IEEE Pes Asia- Pacific Power and Energy Engineering Conference. Washington D C: IEEE, 2016: 1-5.
[14]	Ding K, Feng L, Wang X, et al. Forecast of PV power generation based on residual correction of Markovchain [C] // International Conference on Control, Automation and Information Sciences. Washington D C: IEEE, 2015: 355-359.
Welcome To WUJNS

HOME | Aim and Scope | Editoral Board | Current Issue | Back Issue | Subscribe | Crosscheck | Polishing | Contact us Copyright © 1997-2018 All right reserved