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Energies 2016, 9(11), 941; doi:10.3390/en9110941

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

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
College of Management and Economy, Beijing Institute of Technology, Beijing 100081, China
3
Department of Information Management, Oriental Institute of Technology, New Taipei 220, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Contreras
Received: 8 October 2016 / Revised: 3 November 2016 / Accepted: 4 November 2016 / Published: 11 November 2016
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Abstract

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. View Full-Text
Keywords: electricity demand forecasting; multiple regression (MR); extreme learning machine (ELM); induced ordered weighted harmonic averaging operator (IOWHA); grey relation degree (GRD); carbon emission electricity demand forecasting; multiple regression (MR); extreme learning machine (ELM); induced ordered weighted harmonic averaging operator (IOWHA); grey relation degree (GRD); carbon emission
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liang, Y.; Niu, D.; Cao, Y.; Hong, W.-C. 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 2016, 9, 941.

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