A hybrid procedure for energy demand forecasting in China

Energy

Volumn 37, Issue 1, 2012, Pages 396-404

  Yu, Shi wei ^a,b   Zhu, Ke jun ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

^b BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Energy demand projection; Improved particle swarm optimization genetic algorithm; Path coefficient analysis

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; ECONOMICS; ENERGY UTILIZATION; FORECASTING; GENETIC ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO);

AFFECTING FACTORS; ANT COLONY OPTIMIZATION (ACO); ECONOMIC STRUCTURE; ENERGY DEMAND FORECASTING; ENERGY DEMANDS; ENERGY STRUCTURES; FORECAST METHOD; GROSS DOMESTIC PRODUCTS; HYBRID ALGORITHMS; HYBRID PROCEDURE; INFORMATION TRANSFERS; MULTIPLE LINEAR REGRESSIONS; OPTIMIZATION METHOD; PARTICLE SWARM; PATH-COEFFICIENT ANALYSIS;

FACTOR ANALYSIS;

DEMAND ANALYSIS; ECONOMIC STRUCTURE; ENERGY MARKET; ENERGY USE; GENETIC ALGORITHM; GROSS DOMESTIC PRODUCT; MULTIPLE REGRESSION; OPTIMIZATION; PATH ANALYSIS; POPULATION STRUCTURE; PRECISION; PREDICTION; URBANIZATION;

CHINA;

EID: 84863720895     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2011.11.015     Document Type: Article

References (58)

1. International Energy Agency (IEA) World energy outlook Head of Communication and information Office 2010, France.
2. Smith M., Hargroves K., Stasinopoulos P., Stephens R., Desha C., Hargroves S. Energy transformed: sustainable energy solutions for climate change mitigation 2007, Energy Transformed: Sustainable Energy Solutions for Climate Change Mitigation, 7.
3. National Bureau of Statistics of China (NBSC) China Statistics Yearbook 2010 2010, China Statistics Press, [in Chinese].
4. National Bureau of Statistics of China (NBSC) Annual report on the national economy and social development of P. R. China 2010 2011, China Statistics Press, Beijing, [in Chinese].
5. Erdogdu E. Electricity demand analysis using cointegration and ARIMA modeling: a case study of Turkey. Energy Policy 2007, 35(2):1129-1146.
6. Parikh J., Purohit P., Maitra P. Demand projections of petroleum products and natural gas in India. Energy 2007, 32(10):1825-1837.
7. Zhang M., Mu H., Li G., Ning Y. Forecasting the transport energy demand based on PLSR method in China. Energy 2009, 34(9):1396-1400.
8. Azadeh A., Saberio M., Seraj O. An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran. Energy 2010, 35(6):2351-2366.
9. Limanond T., Jomnonkwao S., Srikaew A. Projection of future transport energy demand of Thailand. Energy Policy 2011, 39(5):2754-2763.
10. Zeng S. Research of energy consumption, economic growth and energy demand forecast on China. Management Review 2011, 23(2):38-44. [in Chinese].
11. Yan X., Crookes R.J. Reduction potentials of energy demand and GHG emissions in China's road transport sector. Energy Policy 2009, 37(2):658-668.
12. Shabbir R., Ahmad S.S. Monitoring urban transport air pollution and energy demand in Rawalpindi and Islamabad using leap model. Energy 2010, 35(5):2323-2332.
13. Huang Y., Jeffrey B.Y., Peng C. The long-term forecast of Taiwan's energy supply and demand: LEAP model application. Energy Policy 2010, doi: 10.1016/ j. enpol.2010.10.023.
14. Zhang J. China's energy demand scenarios and carbon emissions comparison from 2005 to 2020. China Energy 2011, 33(1):33-37. [In Chinese].
15. Hsu C., Chen C. Applications of improved grey prediction model power demand forecasting. Energy Conversion and Management 2003, 44(14):2241-2249.
16. Huang M., He Y., Cen h. Predictive analysis on electric power supply and demand in China. Renew Energy 2007, 32(7):1165-1174.
17. Akay D., Atak M. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy 2007, 32(9):1670-1675.
18. Kumar U., Jain V.K. Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy 2010, 35(4):1709-1716.
19. Murat Y.S., Ceylan H. Use of artificial neural networks for transport energy demand modeling. Energy Policy 2006, 34(17):3165-3172.
20. Ceylan H., Ozturk H.K. Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management 2004, 45(15-16):2525-2537.
21. Toksar M.D. Ant Colony Optimization Approach to Estimate Energy Demand of Turkey. Energy Policy 2007, 35(8):3984-3990.
22. ünler A. Improvement of energy demand forecasts using swarm intelligence: the case of Turkey with projections to 2025. Energy Policy 2008, 36(6):1937-1944.
23. Fang Y. Forecasting combination and encompassing tests. International Journal of Forecasting 2003, 19(1):87-94.
24. Azadeh A., Tarverdian S. Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energy Policy 2007, 35(10):5229-5241.
25. Azadeh A., Ghaderi S.F., Sohrabkhani S. Asimulated-based neural network algorithm for forecasting electrical energy consumption in Iran. Energy Policy 2008, 36(7):2637-2644.
26. Chai J., Guo J., Lu H. Forecasting energy demand of China using Bayesian Combination model. China Population, Resources and Environment 2008, 18(4):50-55.
27. Suo R.X., Wang F.L. The application of combination forecasting model in Chinese energy consumption. Mathematics in Practice and Theory 2010, 40(18):80-85. [In Chinese].
28. Liu A.Q. Forecasting research of energy consumption based on combination model. China population. Resource and Environment 2010, 10(11):25-29. [In Chinese].
29. Lee Y.S., Tong L.L. Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management 2011, 52(1):147-152.
30. Fadare D.A. Modelling of solar energy potential in Nigeria using an artificial neural network model. Applied Energy 2009, 86(9):1410-1422.
31. Geem Z.W., Roper W.E. Energy demand estimation of South Korea using artificial neural network. Energy Policy 2009, 37(10):4049-4054.
32. Azadeh A., Ghaderi S.F., Sohrabkhani S. Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and Management 2008, 49(8):2272-2278.
33. Ekonomou L. Greek long-term energy consumption prediction using artificial neural networks. Energy 2010, 35(8):512-517.
34. Haldenbilen S., Ceylan H. Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy 2005, 33(1):89-98.
35. Canyurt O.E., Ozturk H.K., Hepbaslib A., Utluc Z. Estimating the Turkish residential- commercial energy output based on genetic algorithm (GA) approaches. Energy Policy 2005, 33(8):1011-1019.
36. Canyurt O.E., Ozturk H.K. Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey. Energy Policy 2008, 36(7):2562-2569.
37. Amjadi M.H., Nezamabadi-pour H., Farsangi M.M. Estimation of electricity demand of Iran using two heuristic algorithms. Energy Conversion and Management 2010, 51(3):493-497.
38. Toksar M.D. Estimating the net electricity energy generation and demand using the ant colony optimization approach: case of Turkey. Energy Policy 2009, 37(3):1181-1187.
39. Assareh E., Behrang M.A., Assari M.R., Ghanbarzadeh A. Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 2010, 35(12):5223-5229.
40. Wang K., Salhi A., Fraga E.S. Process design optimization using embedded hybrid visualization and data analysis techniques within a genetic algorithm optimization framework. Chemical Engineering and Processing 2004, 43(5):657-669.
41. Van den Bergh F., Engelbrecht A.P. Acooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 2004, 8(3):225-239.
42. Wright S. The method of path coefficients. The Annals of Mathematical Statistics 1934, 5(3):161-215.
43. Mokhtassi A.B., Akbari G.A., Mirhadi M.J., Zand E., Soufizadeh S. Path analysis of the relationships between seed yield and some morphological and phenological traits in safflower (Carthamus tinctorius L.). Euphytica 2006, 148(3):261-268.
44. Chen D., Zhao C. Particle swarm optimization with adaptive population size and its application. Applied Soft Computing 2009, 9(1):39-48.
45. Shi X.H., Liang Y.C., Lee H.P., Lu C., Wang L.M. An improved GA and a novel PSO-GA-based hybrid algorithm. Information Processing Letters 2005, 93(5):255-261.
46. Gandelli A., Grimaccia F., Mussetta M., Pirinoli P., Zich R.E. Development and validation of different hybridization strategies between GA and PSO. Proceedings of the IEEE Congress on evolutionary Computation, Singapore 2007, 2782-2785.
47. Yao K., Li F., Liu X. Hybrid algorithm based on PSO and GA. Computer Engineering and Applications 2007, 43(6):62-64. [in Chinese].
48. Kao Y., Zahara E. Ahybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing 2008, 8(2):849-857.
49. Kuo R.J., Han Y.S. Ahybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem-A case study on supply chain mode. Applied Mathematical Modelling 2011, 35(8):3905-3917.
50. Valdez F., Melin P., Castillo O. An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms. Applied Soft Computing 2011, 11(2):2625-2632.
51. Clerc M., Kennedy J. The particle swarm: explosion stability and convergence in a multi - dimensional complex space. IEEE Transactions on Evolutionary Computation 2002, 6(1):58-73.
52. Deb K., Beyer H. Self-adaptive genetic algorithms with simulated binary crossover. Evolutionary Computation 2001, 9(2):197-221.
53. Kennedy J., Eberhart R.C. Particle swarm optimization. Proceedings of the IEEE International Conference on neural networks, Perth, Australia 1995, 1942-1947.
54. Howard R.B., Schipper L., Anderson B. The structure and trends and intensity of energy use: trends in five OECD nations. The Energy Journal 1993, 14(2):27-44.
55. Jorgenson D.W., Wilcoxen P.J. Energy, the environment, and economic growth. Handbook of natural resource and energy Economics 1993, vol. III:1267-1349. Elsevier NL, Amsterdam, The Netherlands. A. Kneese, J. Sweeney (Eds.).
56. Wei Y.M., Fang Y., Han Z.Y., Gang Wu Energy economics: modeling and Empirical analysis in China 2006, CRC Press.
57. Andrews-Speed C.P. Energy policy and regulation in the People's Republic of China 2004, Kluwer Law International, The Hague/London.
58. Lewis C. Industrial and business forecasting methods 1982, Butterworth Scientific press, London.