IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v225y2021ics0360544221005193.html
   My bibliography  Save this article

Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods

Author

Listed:
  • Maaouane, Mohamed
  • Zouggar, Smail
  • Krajačić, Goran
  • Zahboune, Hassan

Abstract

Forecasting energy demand for the industrial sector is both interesting and difficult due to the difference in energy demand specific to each industrial sub-sector. For an accurate prediction of the future, Industry Energy Demand model was developed based on multiple linear regression method, using five macroeconomic independent variables. This model was tested by considering Morocco as a study case. Energy demand forecast is based on a bottom-up approach. It is built by piecing together consumed quantity of goods of each sub-sector to give rise to total energy demand. This model produces results comparable to those of the International Energy Agency. Regarding demand forecast, it was found that 8.27 MToe will be needed in 2050 to meet energy demand. It was also found that the adoption of energy efficiency measures allow an energy saving of 1 MToe in 2050. This model was also used to test the impact of variation in import and export on final energy demand. Regarding the potential of the production of biogas from Municipal Solid Waste, it was found that only 36.4% of total Liquefied Petroleum Gas demand could be replaced by biogas.

Suggested Citation

  • Maaouane, Mohamed & Zouggar, Smail & Krajačić, Goran & Zahboune, Hassan, 2021. "Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods," Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:energy:v:225:y:2021:i:c:s0360544221005193
    DOI: 10.1016/j.energy.2021.120270
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221005193
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120270?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. Després, Jacques & Hadjsaid, Nouredine & Criqui, Patrick & Noirot, Isabelle, 2015. "Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools," Energy, Elsevier, vol. 80(C), pages 486-495.
    2. Kunc, Martin & O'Brien, Frances A., 2017. "Exploring the development of a methodology for scenario use: Combining scenario and resource mapping approaches," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 150-159.
    3. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    4. 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.
    5. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    6. Aydin, Celil & Esen, Ömer, 2018. "Does the level of energy intensity matter in the effect of energy consumption on the growth of transition economies? Evidence from dynamic panel threshold analysis," Energy Economics, Elsevier, vol. 69(C), pages 185-195.
    7. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    8. Pardo, Nicolás & Moya, José Antonio & Mercier, Arnaud, 2011. "Prospective on the energy efficiency and CO2 emissions in the EU cement industry," Energy, Elsevier, vol. 36(5), pages 3244-3254.
    9. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    10. Adeoye, Omotola & Spataru, Catalina, 2019. "Modelling and forecasting hourly electricity demand in West African countries," Applied Energy, Elsevier, vol. 242(C), pages 311-333.
    11. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    12. Pardo, Nicolás & Moya, José Antonio, 2013. "Prospective scenarios on energy efficiency and CO2 emissions in the European Iron & Steel industry," Energy, Elsevier, vol. 54(C), pages 113-128.
    13. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    14. Bhadbhade, Navdeep & Zuberi, M. Jibran S. & Patel, Martin K., 2019. "A bottom-up analysis of energy efficiency improvement and CO2 emission reduction potentials for the swiss metals sector," Energy, Elsevier, vol. 181(C), pages 173-186.
    15. Köne, Aylin Çigdem & Büke, Tayfun, 2010. "Forecasting of CO2 emissions from fuel combustion using trend analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2906-2915, December.
    16. Guang, Fengtao & He, Yongxiu & Wen, Le & Sharp, Basil, 2019. "Energy intensity and its differences across China’s regions: Combining econometric and decomposition analysis," Energy, Elsevier, vol. 180(C), pages 989-1000.
    17. Di Leo, Senatro & Caramuta, Pietro & Curci, Paola & Cosmi, Carmelina, 2020. "Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models," Energy, Elsevier, vol. 196(C).
    18. Fais, Birgit & Sabio, Nagore & Strachan, Neil, 2016. "The critical role of the industrial sector in reaching long-term emission reduction, energy efficiency and renewable targets," Applied Energy, Elsevier, vol. 162(C), pages 699-712.
    19. Tso, Geoffrey K.F. & Guan, Jingjing, 2014. "A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption," Energy, Elsevier, vol. 66(C), pages 722-731.
    20. Oda, Junichiro & Akimoto, Keigo & Sano, Fuminori & Tomoda, Toshimasa, 2007. "Diffusion of energy efficient technologies and CO2 emission reductions in iron and steel sector," Energy Economics, Elsevier, vol. 29(4), pages 868-888, July.
    21. Farajzadeh, Zakariya & Nematollahi, Mohammad Amin, 2018. "Energy intensity and its components in Iran: Determinants and trends," Energy Economics, Elsevier, vol. 73(C), pages 161-177.
    22. Chen, Jing-Ming & Yu, Biying & Wei, Yi-Ming, 2018. "Energy technology roadmap for ethylene industry in China," Applied Energy, Elsevier, vol. 224(C), pages 160-174.
    23. 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.
    24. Fleiter, Tobias & Worrell, Ernst & Eichhammer, Wolfgang, 2011. "Barriers to energy efficiency in industrial bottom-up energy demand models--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3099-3111, August.
    25. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
    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. Eskandari, Hamidreza & Saadatmand, Hassan & Ramzan, Muhammad & Mousapour, Mobina, 2024. "Innovative framework for accurate and transparent forecasting of energy consumption: A fusion of feature selection and interpretable machine learning," Applied Energy, Elsevier, vol. 366(C).
    2. Anna Borucka, 2023. "Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth," Sustainability, MDPI, vol. 15(9), pages 1-21, April.
    3. Xiong, Pingping & Li, Kailing & Shu, Hui & Wang, Junjie, 2021. "Forecast of natural gas consumption in the Asia-Pacific region using a fractional-order incomplete gamma grey model," Energy, Elsevier, vol. 237(C).
    4. Qiao, Qingyao & Eskandari, Hamidreza & Saadatmand, Hassan & Sahraei, Mohammad Ali, 2024. "An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector," Energy, Elsevier, vol. 286(C).
    5. Demirci, Alpaslan & Öztürk, Zafer & Tercan, Said Mirza, 2023. "Decision-making between hybrid renewable energy configurations and grid extension in rural areas for different climate zones," Energy, Elsevier, vol. 262(PA).
    6. Varbanov, Petar Sabev & Wang, Bohong & Ocłoń, Paweł & Radziszewska-Zielina, Elżbieta & Ma, Ting & Klemeš, Jiří Jaromír & Jia, Xuexiu, 2023. "Efficiency measures for energy supply and use aiming for a clean circular economy," Energy, Elsevier, vol. 283(C).
    7. Oh, Jiyoung & Min, Daiki, 2024. "Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics," Energy, Elsevier, vol. 300(C).
    8. Arthit Champeecharoensuk & Shobhakar Dhakal & Nuwong Chollacoop, 2023. "Climate Change Mitigation in Thailand’s Domestic Aviation: Mitigation Options Analysis towards 2050," Energies, MDPI, vol. 16(20), pages 1-20, October.
    9. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.

    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. Di Leo, Senatro & Caramuta, Pietro & Curci, Paola & Cosmi, Carmelina, 2020. "Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models," Energy, Elsevier, vol. 196(C).
    2. Chang, Miguel & Thellufsen, Jakob Zink & Zakeri, Behnam & Pickering, Bryn & Pfenninger, Stefan & Lund, Henrik & Østergaard, Poul Alberg, 2021. "Trends in tools and approaches for modelling the energy transition," Applied Energy, Elsevier, vol. 290(C).
    3. Kialashaki, Arash & Reisel, John R., 2014. "Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States," Energy, Elsevier, vol. 76(C), pages 749-760.
    4. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Mardones, Cristian, 2021. "Ex-post evaluation and cost-benefit analysis of a heater replacement program implemented in southern Chile," Energy, Elsevier, vol. 227(C).
    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. Yazdanie, M. & Orehounig, K., 2021. "Advancing urban energy system planning and modeling approaches: Gaps and solutions in perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    8. Savvidis, Georgios & Siala, Kais & Weissbart, Christoph & Schmidt, Lukas & Borggrefe, Frieder & Kumar, Subhash & Pittel, Karen & Madlener, Reinhard & Hufendiek, Kai, 2019. "The gap between energy policy challenges and model capabilities," Energy Policy, Elsevier, vol. 125(C), pages 503-520.
    9. Mahesh, Aeidapu & Sandhu, Kanwarjit Singh, 2015. "Hybrid wind/photovoltaic energy system developments: Critical review and findings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1135-1147.
    10. Scheller, Fabian & Bruckner, Thomas, 2019. "Energy system optimization at the municipal level: An analysis of modeling approaches and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 444-461.
    11. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    12. Brunke, Jean-Christian & Blesl, Markus, 2014. "A plant-specific bottom-up approach for assessing the cost-effective energy conservation potential and its ability to compensate rising energy-related costs in the German iron and steel industry," Energy Policy, Elsevier, vol. 67(C), pages 431-446.
    13. Chanthawong, Anuman & Dhakal, Shobhakar & Jongwanich, Juthathip, 2016. "Supply and demand of biofuels in the fuel market of Thailand: Two stage least square and three least square approaches," Energy, Elsevier, vol. 114(C), pages 431-443.
    14. Prasad, Ravita D. & Bansal, R.C. & Raturi, Atul, 2014. "Multi-faceted energy planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 686-699.
    15. 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).
    16. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    17. Pukšec, Tomislav & Mathiesen, Brian Vad & Novosel, Tomislav & Duić, Neven, 2014. "Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia," Energy, Elsevier, vol. 76(C), pages 198-209.
    18. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    19. Jasiński, Tomasz, 2022. "A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    20. Pan, Xiongfeng & Uddin, Md. Kamal & Saima, Umme & Jiao, Zhiming & Han, Cuicui, 2019. "How do industrialization and trade openness influence energy intensity? Evidence from a path model in case of Bangladesh," Energy Policy, Elsevier, vol. 133(C).

    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:energy:v:225:y:2021:i:c:s0360544221005193. 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.journals.elsevier.com/energy .

    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.