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

Yi Liang, Dongxiao Niu, Ye Cao and Wei-Chiang Hong ()
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Yi Liang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Ye Cao: College of Management and Economy, Beijing Institute of Technology, Beijing 100081, China

Energies, 2016, vol. 9, issue 11, 1-22

Abstract: The power industry is the main battlefield of CO 2 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.

Keywords: electricity demand forecasting; multiple regression (MR); extreme learning machine (ELM); induced ordered weighted harmonic averaging operator (IOWHA); grey relation degree (GRD); carbon emission (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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