Earticle

초록

영어

Since the development of high energy-consuming industries has an important impact on the total electricity consumption, it is essential to predict the electricity demand of these industries. A procedure based on LS-SVM algorithm and scenario analysis is proposed to forecast the electricity consumption of high energy-intensive industries in Inner Mongolia, which takes three affecting factors into consideration, including the output value, the proportion of output accounting for GDP, and electricity consumption intensity. The prediction results show that the prediction accuracy of LS-SVM is rather high with an average error rate of 2.03%. The electricity demand of energy-intensive industries in Inner Mongolia will reach 2136.9~2175.1 million kWh in 2015, and reach 2514.4 ~ 2966.9 million kWh in 2020. Meanwhile, the annual growth rate among 2013 to 2020 will be 5.18%~7.48%. In addition, 377.5 million kWh will be saved in 2015, and 791.8 million kWh will be saved in 2020.

목차

Abstract
 1. Introduction
 2. LS-SVM Algorithm
 3. Electricity Demand Forecasting For Energy-Intensive Industries
  3.1. Variable Selection and Data Sources
  3.2. The Analysis of Development Scenarios
  3.3. Analysis of Prediction Results
 4. Conclusions and Recommendations
  4.1. Conclusions
  4.2. Recommendations
 References

키워드

저자

• Huiru Zhao [ School of Economics and Management, North China Electric Power University ]
• Zhao Ma [ School of Economics and Management, North China Electric Power University ]
• Nana Li [ School of Economics and Management, North China Electric Power University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605