Application of carbon emissions prediction using least squares support vector machine based on grid search

WSEAS Transactions on Systems and Control

Volumn 10, Issue , 2015, Pages 95-104

  Sun, Wei ^a   Liang, Yi ^a   Xu, Yanfeng ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Carbon emissions prediction; Grid search optimization; Least squares support vector machine

Indexed keywords

CARBON; ELECTRIC LOAD FORECASTING; ENERGY UTILIZATION; FORECASTING; GREENHOUSE EFFECT; GREENHOUSES; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; VECTORS;

BP NEURAL NETWORK MODEL; CARBON EMISSIONS; GRID SEARCH; INDEPENDENT VARIABLES; LEAST SQUARES SUPPORT VECTOR MACHINES; MEAN ABSOLUTE PERCENTAGE ERROR; REGULARIZATION PARAMETERS; VALUE ADDED SERVICE;

LEAST SQUARES APPROXIMATIONS;

EID: 84926478989     PISSN: 19918763     EISSN: 22242856     Source Type: Journal    
DOI: None     Document Type: Article

References (10)

1. Juan Antonio Duro, Weighting vectors and international inequality changes in environmental indicators: An analysis of CO2 per capita emissions and Kaya factors, Energy Economics, Vol. 39, 2013, pp.122-127.
2. Janis Brizga, Kuishuang Feng, Klaus Hubacek, Drivers of CO2 emissions in the former Soviet Union: A country level IPAT analysis from 1990 to 2010, Energy, Vol.59, No.15, 2013, pp. 743-753.
3. Ping Wang, Wanshui Wu, Bangzhu Zhu, Yiming Wei, Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China, Applied Energy, Vol.106, 2013, pp. 65-71.
4. G.Q. Chen, Shan Guo, Ling Shao, J.S. Li, Zhan-Ming Chen, Three-scale input-output modeling for urban economy: carbon emissions by Beijing 2007, Communications in Nonlinear Science and Numerical Simulation, Vol.18, No.9, 2013, pp. 2493-2506.
5. Wenying Chen, The costs of mitigating carbon emissionss in China: findings from China MARKAL-MACRO modeling, Energy Policy, Vol.33, No.7, 2005, pp. 885-896.
6. Panida Thepkhun, Bundit Limmeechokchai, Shinichiro Fujimori, Toshihiko Masui, Ram M. Shrestha, Thailand's Low-Carbon Scenario 2050: The AIM/CGE analyses of CO2 mitigation measures, Energy Policy, Vol.62, 2013, pp. 561-572.
7. Yanxi Zhang, Xiangdong Gao, Seiji Katayama, Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding, Journal of Manufacturing Systems, Vol.34, 2015, pp. 53-59.
8. Vapnik V. The nature of statistic learning theory. New York: Springer-Verlag;1995.
9. Vapnik V. Statistical learning theory. New York: Wiley; 1998.
10. Wei Sun, Yi Liang, Research of least squares support vector regression based on differential evolution algorithm in short-term load forecasting model, Journal of Renewable and Sustainable Energy, Vol.6, No.5, 2014, pp. 053137.