IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v30y2014icp661-671.html
   My bibliography  Save this article

Review of energy models to the development of an efficient industrial energy model

Author

Listed:
  • Olanrewaju, O.A
  • Jimoh, A.A

Abstract

Presently, there are huge challenges in the presence of the global energy sector, especially in the energy intensive industries that entail a huge collection of energy use, which makes energy security a vital worry. This study analyses various energy models, taking into consideration their various gaps which led to the development of an integrated model for assessing energy efficiency potential in the industrial sector. The resulting developed model will not only serve as a tool for long-term planning to ensure that energy supply is available to meet the demands of targeted economic growth, it will also give policy-makers in the industrial energy management an alertness on how to monitor, control and manage energy consumption.

Suggested Citation

  • Olanrewaju, O.A & Jimoh, A.A, 2014. "Review of energy models to the development of an efficient industrial energy model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 661-671.
    • Handle: RePEc:eee:rensus:v:30:y:2014:i:c:p:661-671
    DOI: 10.1016/j.rser.2013.11.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032113007569
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2013.11.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cooper, William W. & Ruiz, Jose L. & Sirvent, Inmaculada, 2007. "Choosing weights from alternative optimal solutions of dual multiplier models in DEA," European Journal of Operational Research, Elsevier, vol. 180(1), pages 443-458, July.
    2. Barros, Carlos Pestana, 2008. "Efficiency analysis of hydroelectric generating plants: A case study for Portugal," Energy Economics, Elsevier, vol. 30(1), pages 59-75, January.
    3. Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2012. "Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors," Energy, Elsevier, vol. 46(1), pages 629-635.
    4. Hatzigeorgiou, Emmanouil & Polatidis, Heracles & Haralambopoulos, Dias, 2008. "CO2 emissions in Greece for 1990–2002: A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques," Energy, Elsevier, vol. 33(3), pages 492-499.
    5. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    6. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    7. Sözen, Adnan & Arcaklioglu, Erol & Özkaymak, Mehmet, 2005. "Turkey's net energy consumption," Applied Energy, Elsevier, vol. 81(2), pages 209-221, June.
    8. Ang, B. W. & Lee, P. W., 1996. "Decomposition of industrial energy consumption: The energy coefficient approach," Energy Economics, Elsevier, vol. 18(1-2), pages 129-143, April.
    9. Azadeh, A. & Sheikhalishahi, M. & Asadzadeh, S.M., 2011. "A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity," Renewable Energy, Elsevier, vol. 36(12), pages 3394-3401.
    10. Azadeh, A. & Amalnick, M.S. & Ghaderi, S.F. & Asadzadeh, S.M., 2007. "An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors," Energy Policy, Elsevier, vol. 35(7), pages 3792-3806, July.
    11. Liu, C.H. & Lin, Sue J. & Lewis, Charles, 2010. "Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis," Energy Policy, Elsevier, vol. 38(2), pages 1049-1058, February.
    12. Sozen, Adnan & Gulseven, Zafer & Arcaklioglu, Erol, 2007. "Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies," Energy Policy, Elsevier, vol. 35(12), pages 6491-6505, December.
    13. Ang, B. W., 1995. "Multilevel decomposition of industrial energy consumption," Energy Economics, Elsevier, vol. 17(1), pages 39-51, January.
    14. Richard B. Howarth & Lee Schipper, 1991. "Manufacturing Energy Use in Eight OECD Countries: Trends through 1988," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 15-40.
    15. Sarıca, Kemal & Or, Ilhan, 2007. "Efficiency assessment of Turkish power plants using data envelopment analysis," Energy, Elsevier, vol. 32(8), pages 1484-1499.
    16. Salta, Myrsine & Polatidis, Heracles & Haralambopoulos, Dias, 2009. "Energy use in the Greek manufacturing sector: A methodological framework based on physical indicators with aggregation and decomposition analysis," Energy, Elsevier, vol. 34(1), pages 90-111.
    17. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    18. Azadeh, A. & Ghaderi, S.F. & Omrani, H. & Eivazy, H., 2009. "An integrated DEA-COLS-SFA algorithm for optimization and policy making of electricity distribution units," Energy Policy, Elsevier, vol. 37(7), pages 2605-2618, July.
    19. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 2011. "A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE," Applied Energy, Elsevier, vol. 88(11), pages 3850-3859.
    20. Gillingham, Kenneth & Newell, Richard G. & Palmer, Karen L., 2004. "Retrospective Examination of Demand-Side Energy Efficiency Policies," Discussion Papers 10477, Resources for the Future.
    21. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    22. Vaninsky, Alexander, 2006. "Efficiency of electric power generation in the United States: Analysis and forecast based on data envelopment analysis," Energy Economics, Elsevier, vol. 28(3), pages 326-338, May.
    23. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    24. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    25. Sozen, Adnan & Arcaklioglu, Erol, 2007. "Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey," Energy Policy, Elsevier, vol. 35(10), pages 4981-4992, October.
    26. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    27. Liu, Lan-Cui & Fan, Ying & Wu, Gang & Wei, Yi-Ming, 2007. "Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: An empirical analysis," Energy Policy, Elsevier, vol. 35(11), pages 5892-5900, November.
    28. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    29. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
    30. Hu, Jin-Li & Kao, Chih-Hung, 2007. "Efficient energy-saving targets for APEC economies," Energy Policy, Elsevier, vol. 35(1), pages 373-382, January.
    31. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    32. Ermis, K. & Midilli, A. & Dincer, I. & Rosen, M.A., 2007. "Artificial neural network analysis of world green energy use," Energy Policy, Elsevier, vol. 35(3), pages 1731-1743, March.
    33. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
    34. Pandey, Rahul, 2002. "Energy policy modelling: agenda for developing countries," Energy Policy, Elsevier, vol. 30(2), pages 97-106, January.
    35. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    36. Saygin, D. & Worrell, E. & Patel, M.K. & Gielen, D.J., 2011. "Benchmarking the energy use of energy-intensive industries in industrialized and in developing countries," Energy, Elsevier, vol. 36(11), pages 6661-6673.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Perroni, Marcos G. & Gouvea da Costa, Sergio E. & Pinheiro de Lima, Edson & Vieira da Silva, Wesley & Tortato, Ubiratã, 2018. "Measuring energy performance: A process based approach," Applied Energy, Elsevier, vol. 222(C), pages 540-553.
    2. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    3. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    4. Mawson, Victoria Jayne & Hughes, Ben Richard, 2021. "Optimisation of HVAC control and manufacturing schedules for the reduction of peak energy demand in the manufacturing sector," Energy, Elsevier, vol. 227(C).
    5. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    6. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    7. Federico Scarpa & Vincenzo Bianco, 2017. "Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector," Energies, MDPI, vol. 10(11), pages 1-13, November.
    8. Yoro, Kelvin O. & Daramola, Michael O. & Sekoai, Patrick T. & Wilson, Uwemedimo N. & Eterigho-Ikelegbe, Orevaoghene, 2021. "Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    9. Md. Nasimul Islam Maruf, 2019. "Sector Coupling in the North Sea Region—A Review on the Energy System Modelling Perspective," Energies, MDPI, vol. 12(22), pages 1-35, November.
    10. Alexander Melnik & Kirill Ermolaev, 2020. "Strategy Context of Decision Making for Improved Energy Efficiency in Industrial Energy Systems," Energies, MDPI, vol. 13(7), pages 1-28, March.
    11. Singpai, Bodin & Wu, Desheng Dash, 2021. "An integrative approach for evaluating the environmental economic efficiency," Energy, Elsevier, vol. 215(PB).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    2. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    3. Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2013. "Assessing the energy potential in the South African industry: A combined IDA-ANN-DEA (Index Decomposition Analysis-Artificial Neural Network-Data Envelopment Analysis) model," Energy, Elsevier, vol. 63(C), pages 225-232.
    4. Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2012. "Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors," Energy, Elsevier, vol. 46(1), pages 629-635.
    5. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    6. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    7. Yeo, In-Ae & Yee, Jurng-Jae, 2014. "A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artifi," Applied Energy, Elsevier, vol. 119(C), pages 99-117.
    8. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    9. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
    10. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.
    11. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    12. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
    13. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    14. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    15. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    16. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    17. Chen, Jiandong & Cheng, Shulei & Song, Malin, 2018. "Changes in energy-related carbon dioxide emissions of the agricultural sector in China from 2005 to 2013," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 748-761.
    18. Fernández González, P. & Landajo, M. & Presno, M.J., 2014. "Tracking European Union CO2 emissions through LMDI (logarithmic-mean Divisia index) decomposition. The activity revaluation approach," Energy, Elsevier, vol. 73(C), pages 741-750.
    19. Zhang, Xing-Ping & Tan, Ya-Kun & Tan, Qin-Liang & Yuan, Jia-Hai, 2012. "Decomposition of aggregate CO2 emissions within a joint production framework," Energy Economics, Elsevier, vol. 34(4), pages 1088-1097.
    20. Chen, Zhongfei & Barros, Carlos Pestana & Borges, Maria Rosa, 2015. "A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies," Energy Economics, Elsevier, vol. 48(C), pages 136-144.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:30:y:2014:i:c:p:661-671. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.