본문 바로가기
KERD
논문KCI 등재

Identification of High Significance Product Items Through the Analysis of Energy Consumption in Steel Factory

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류학제간연구

초록

Analysis and modeling of industrial energy are currently a time-insertion process because more and more energy is consumed for economic growth in an industrial factory. Industrial energy consumption analysis and prediction play a very important role in improving the energy utilization rate to make profitable things for industrial companies or factories. Industrial factory owners are also starting to realize the importance of energy analysis and predictions to make a plan for better energy utilization for their factories. So, this is the high time to work as a technological supportive hand with an industrial factory in order to improve their energy utilization for making some profitable things for industrial organizations and also for our society. It is not so easy process to analyses energy and realizes accurate predictions of industrial energy consumption. Therefore, this study deals with analysing total usages of 24-hours energy consumption for every month and also total usages of energy for the year 2017. We find out the difference between total usages of energy and demands of energy for the year 2017. We make relation curve between per-day usages of energy and different types of materials weight for different types of manufacturing product, and also make a relation curve between per-day usages of energy, demands of energy and total materials weight for manufacturing products for the year 2017. We use the correlation method to find out the relationship of usages of energy with different types of materials weight for manufacturing products. Finally, we find out the total usages of energy for manufacturing every product and make a decision about the energy utilization.

Abstract

Analysis and modeling of industrial energy are currently a time-insertion process because more and more energy is consumed for economic growth in an industrial factory. Industrial energy consumption analysis and prediction play a very important role in improving the energy utilization rate to make profitable things for industrial companies or factories. Industrial factory owners are also starting to realize the importance of energy analysis and predictions to make a plan for better energy utilization for their factories. So, this is the high time to work as a technological supportive hand with an industrial factory in order to improve their energy utilization for making some profitable things for industrial organizations and also for our society. It is not so easy process to analyses energy and realizes accurate predictions of industrial energy consumption. Therefore, this study deals with analysing total usages of 24-hours energy consumption for every month and also total usages of energy for the year 2017. We find out the difference between total usages of energy and demands of energy for the year 2017. We make relation curve between per-day usages of energy and different types of materials weight for different types of manufacturing product, and also make a relation curve between per-day usages of energy, demands of energy and total materials weight for manufacturing products for the year 2017. We use the correlation method to find out the relationship of usages of energy with different types of materials weight for manufacturing products. Finally, we find out the total usages of energy for manufacturing every product and make a decision about the energy utilization.

참고문헌 (18)

  1. S-H. Park, "Decomposition of industrial energy consumptions: An alternative method", Energy Economics, 14(4), pp.265~270 (1992)
  2. W-P. Yemane, "Disaggregated industrial energy consumption and GDP: the case of SHANGhai, 1952", Energy Economics, 26(1), pp.69~75 (2004)
  3. R. Sari, "The relationship between disaggregate energy consumption and industrial production in the United States: An ARDL approach", Energy Economics, 30(5), pp.2302~2313 (2008)
  4. Z-X. Wang, "An improved grey multivariable for predicting industrial energy consumption in China", Applied Mathematical Modelling, 40(11-12), pp.5745~5758 (2016)
  5. L. M. Candanedo, "Data driven prediction models of energy use of appliances in a low energy house", Energy and Buildings, 140, pp.81~97 (2017)
  6. Y-J. Zhang, "The decomposition of energy-related carbon emission and its decoupling with economic growth in China", Renewable and Sustainable Energy Reviews, 41, pp.1255~1266 (2015)
  7. Z-F. Mi, "Potential impacts of industrial structure on energy consumption and CO2 emission: acase study of beijing", Journal of Cleaner Production, 103, pp.455~462 (2015)
  8. Y. Liu, "Assessing the impact of population, income and technology on energy consumption and industrial pollutant emission in China", Applied Energy, 155, pp.904~917 (2015)
  9. E. Duran, "Analysis and decomposition of energy consumption in the Chilean industry", Energy Policy, 86, pp.552~561 (2015)
  10. Q. Wang, "Measurement and decomposition of energy –saving and emissions reduction performance in Chinese cities", Applied Energy, 151, pp.85~92 (2015)
  11. E. Dezendeh, "The impact of occupants’ behaviours on building energy analysis: A research review", Renewable and Sustainable Energy Reviews, 80, pp.1061~1071 (2017)
  12. F. Ascione, "A new methodology of the multi-objectives optimization of building energy performance", Energy and Buildings, 88, pp.78~90 (2015)
  13. J. Pan, "An internet of things framework for smart energy in building:Designs, prototype, and experiments", IEEE Internet of things Journal, 2(6), pp.527~537 (2015)
  14. E. Yalcinkaya, "Patterns and trends in building information modeling (BIM) research: A latent semantic analysis", Automation in Construction, 59, pp.68~80 (2015)
  15. S. Narayan, "An investigation of renewable and non –renewable energy consumption and economic growth nexus using industrial and residential consumption", Energy Economics, 68, pp.160~176 (2017)
  16. Q. He, "Mapping the managerial areas of Building Information Modeling (BIM)using scientometric analysis", International Journal of Project Management, 35(4), pp.670~685 (2017)
  17. "Method for correlation analysis from STHDA" (2019)[URL]
  18. "Electricity Pricing in South Korea from gtm" (2019)[URL]

관련 논문AI추천

이 간행물의 다른 논문 (8건)