Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission
The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-11-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/11/941 |
| _version_ | 1798003684493230080 |
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| author | Yi Liang Dongxiao Niu Ye Cao Wei-Chiang Hong |
| author_facet | Yi Liang Dongxiao Niu Ye Cao Wei-Chiang Hong |
| author_sort | Yi Liang |
| collection | DOAJ |
| description | The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD) with induced ordered weighted harmonic averaging operator (IOWHA) to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM) forecasting model and multiple regression (MR) model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure. |
| first_indexed | 2024-04-11T12:11:39Z |
| format | Article |
| id | doaj.art-5b679251748a49fe9b9282e39f8c56ac |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T12:11:39Z |
| publishDate | 2016-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5b679251748a49fe9b9282e39f8c56ac2022-12-22T04:24:36ZengMDPI AGEnergies1996-10732016-11-0191194110.3390/en9110941en9110941Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon EmissionYi Liang0Dongxiao Niu1Ye Cao2Wei-Chiang Hong3School of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaCollege of Management and Economy, Beijing Institute of Technology, Beijing 100081, ChinaDepartment of Information Management, Oriental Institute of Technology, New Taipei 220, TaiwanThe power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD) with induced ordered weighted harmonic averaging operator (IOWHA) to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM) forecasting model and multiple regression (MR) model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.http://www.mdpi.com/1996-1073/9/11/941electricity demand forecastingmultiple regression (MR)extreme learning machine (ELM)induced ordered weighted harmonic averaging operator (IOWHA)grey relation degree (GRD)carbon emission |
| spellingShingle | Yi Liang Dongxiao Niu Ye Cao Wei-Chiang Hong Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission Energies electricity demand forecasting multiple regression (MR) extreme learning machine (ELM) induced ordered weighted harmonic averaging operator (IOWHA) grey relation degree (GRD) carbon emission |
| title | Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission |
| title_full | Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission |
| title_fullStr | Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission |
| title_full_unstemmed | Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission |
| title_short | Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission |
| title_sort | analysis and modeling for china s electricity demand forecasting using a hybrid method based on multiple regression and extreme learning machine a view from carbon emission |
| topic | electricity demand forecasting multiple regression (MR) extreme learning machine (ELM) induced ordered weighted harmonic averaging operator (IOWHA) grey relation degree (GRD) carbon emission |
| url | http://www.mdpi.com/1996-1073/9/11/941 |
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