Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China
Path-coefficient analysis is utilized to investigate the direct and indirect effects of economic growth, population growth, urbanization rate, industrialization level, and carbon intensity on electricity demand of China. To improve the projection accuracy of electricity demand, this study proposes a...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2017/2379381 |
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| author | Qunli Wu Chenyang Peng |
| author_facet | Qunli Wu Chenyang Peng |
| author_sort | Qunli Wu |
| collection | DOAJ |
| description | Path-coefficient analysis is utilized to investigate the direct and indirect effects of economic growth, population growth, urbanization rate, industrialization level, and carbon intensity on electricity demand of China. To improve the projection accuracy of electricity demand, this study proposes a hybrid bat algorithm, Gaussian perturbations, and simulated annealing (BAG-SA) optimization method. The proposed BAG-SA algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality but also enhances local search ability and speeds up the global convergence rate. The BAG-SA algorithm is employed to optimize the coefficients of the multiple linear and quadratic forms of electricity demand estimation model. Results indicate that the proposed algorithm has higher precision and reliability than the coefficients optimized by other single-optimization methods, such as genetic algorithm, particle swarm optimization algorithm, or bat algorithm. And the quadratic form of BAG-SA electricity demand estimation model has better fitting ability compared with the multiple linear form of the model. Therefore, the quadratic form of the model is applied to estimate electricity demand of China from 2016 to 2030. The findings of this study demonstrate that China’s electricity demand will reach 14925200 million KWh in 2030. |
| format | Article |
| id | doaj-art-1188d65fcfe646cfa4bd508a73e778c8 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-1188d65fcfe646cfa4bd508a73e778c82025-08-20T02:01:54ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/23793812379381Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of ChinaQunli Wu0Chenyang Peng1Department of Economics and Management, North China Electric Power University, Baoding 071003, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071003, ChinaPath-coefficient analysis is utilized to investigate the direct and indirect effects of economic growth, population growth, urbanization rate, industrialization level, and carbon intensity on electricity demand of China. To improve the projection accuracy of electricity demand, this study proposes a hybrid bat algorithm, Gaussian perturbations, and simulated annealing (BAG-SA) optimization method. The proposed BAG-SA algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality but also enhances local search ability and speeds up the global convergence rate. The BAG-SA algorithm is employed to optimize the coefficients of the multiple linear and quadratic forms of electricity demand estimation model. Results indicate that the proposed algorithm has higher precision and reliability than the coefficients optimized by other single-optimization methods, such as genetic algorithm, particle swarm optimization algorithm, or bat algorithm. And the quadratic form of BAG-SA electricity demand estimation model has better fitting ability compared with the multiple linear form of the model. Therefore, the quadratic form of the model is applied to estimate electricity demand of China from 2016 to 2030. The findings of this study demonstrate that China’s electricity demand will reach 14925200 million KWh in 2030.http://dx.doi.org/10.1155/2017/2379381 |
| spellingShingle | Qunli Wu Chenyang Peng Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China Discrete Dynamics in Nature and Society |
| title | Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China |
| title_full | Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China |
| title_fullStr | Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China |
| title_full_unstemmed | Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China |
| title_short | Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China |
| title_sort | electricity demand projection using a path coefficient analysis and bag sa approach a case study of china |
| url | http://dx.doi.org/10.1155/2017/2379381 |
| work_keys_str_mv | AT qunliwu electricitydemandprojectionusingapathcoefficientanalysisandbagsaapproachacasestudyofchina AT chenyangpeng electricitydemandprojectionusingapathcoefficientanalysisandbagsaapproachacasestudyofchina |