IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v84y2015icp816-824.html
   My bibliography  Save this article

Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks

Author

Listed:
  • Antanasijević, Davor
  • Pocajt, Viktor
  • Ristić, Mirjana
  • Perić-Grujić, Aleksandra

Abstract

This paper presents a new approach for the estimation of energy-related GHG (greenhouse gas) emissions at the national level that combines the simplicity of the concept of GHG intensity and the generalization capabilities of ANNs (artificial neural networks). The main objectives of this work includes the determination of the accuracy of a GRNN (general regression neural network) model applied for the prediction of EC (energy consumption) and GHG intensity of energy consumption, utilizing general country statistics as inputs, as well as analysis of the accuracy of energy-related GHG emissions obtained by multiplying the two aforementioned outputs. The models were developed using historical data from the period 2004–2012, for a set of 26European countries (EU Members). The obtained results demonstrate that the GRNN GHG intensity model provides a more accurate prediction, with the MAPE (mean absolute percentage error) of 4.5%, than tested MLR (multiple linear regression) and second-order and third-order non-linear MPR (multiple polynomial regression) models. Also, the GRNN EC model has high accuracy (MAPE = 3.6%), and therefore both GRNN models and the proposed approach can be considered as suitable for the calculation of GHG emissions. The energy-related predicted GHG emissions were very similar to the actual GHG emissions of EU Members (MAPE = 6.4%).

Suggested Citation

  • Antanasijević, Davor & Pocajt, Viktor & Ristić, Mirjana & Perić-Grujić, Aleksandra, 2015. "Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks," Energy, Elsevier, vol. 84(C), pages 816-824.
    • Handle: RePEc:eee:energy:v:84:y:2015:i:c:p:816-824
    DOI: 10.1016/j.energy.2015.03.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215003667
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.03.060?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. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    2. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Yousefi, Marziye & Movahedi, Mehran, 2013. "Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks," Energy, Elsevier, vol. 52(C), pages 333-338.
    3. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    4. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    5. Chicco, Gianfranco & Mancarella, Pierluigi, 2008. "Assessment of the greenhouse gas emissions from cogeneration and trigeneration systems. Part I: Models and indicators," Energy, Elsevier, vol. 33(3), pages 410-417.
    6. Xu, Xianshuo & Zhao, Tao & Liu, Nan & Kang, Jidong, 2014. "Changes of energy-related GHG emissions in China: An empirical analysis from sectoral perspective," Applied Energy, Elsevier, vol. 132(C), pages 298-307.
    7. Mancarella, Pierluigi & Chicco, Gianfranco, 2008. "Assessment of the greenhouse gas emissions from cogeneration and trigeneration systems. Part II: Analysis techniques and application cases," Energy, Elsevier, vol. 33(3), pages 418-430.
    8. Lee, Won-Yong & House, John M. & Kyong, Nam-Ho, 2004. "Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks," Applied Energy, Elsevier, vol. 77(2), pages 153-170, February.
    9. Akimoto, Keigo & Sano, Fuminori & Homma, Takashi & Oda, Junichiro & Nagashima, Miyuki & Kii, Masanobu, 2010. "Estimates of GHG emission reduction potential by country, sector, and cost," Energy Policy, Elsevier, vol. 38(7), pages 3384-3393, July.
    10. Rentziou, Aikaterini & Gkritza, Konstantina & Souleyrette, Reginald R., 2012. "VMT, energy consumption, and GHG emissions forecasting for passenger transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 487-500.
    11. Villalba, Gara & Gemechu, Eskinder Demisse, 2011. "Estimating GHG emissions of marine ports--the case of Barcelona," Energy Policy, Elsevier, vol. 39(3), pages 1363-1368, March.
    12. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    13. Bhattacharyya, Subhes C. & Matsumura, Wataru, 2010. "Changes in the GHG emission intensity in EU-15: Lessons from a decomposition analysis," Energy, Elsevier, vol. 35(8), pages 3315-3322.
    14. Ben-Nakhi, Abdullatif E. & Mahmoud, Mohamed A., 2002. "Energy conservation in buildings through efficient A/C control using neural networks," Applied Energy, Elsevier, vol. 73(1), pages 5-23, September.
    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. Paweł Dec & Jacek Wysocki, 2022. "In Search of Non-Obvious Relationships between Greenhouse Gas or Particulate Matter Emissions, Renewable Energy and Corruption," Energies, MDPI, vol. 15(4), pages 1-20, February.
    2. Yang, Tao & Pan, Yiqun & Yang, Yikun & Lin, Meishun & Qin, Bingyue & Xu, Peng & Huang, Zhizhong, 2017. "CO2 emissions in China's building sector through 2050: A scenario analysis based on a bottom-up model," Energy, Elsevier, vol. 128(C), pages 208-223.
    3. Mahmoud Abdelkader Bashery Abbass & Mohamed Hamdy, 2021. "A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain," Energies, MDPI, vol. 14(17), pages 1-30, August.
    4. Liobikienė, Genovaitė & Butkus, Mindaugas, 2017. "The European Union possibilities to achieve targets of Europe 2020 and Paris agreement climate policy," Renewable Energy, Elsevier, vol. 106(C), pages 298-309.
    5. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    6. Su, Meirong & Pauleit, Stephan & Yin, Xuemei & Zheng, Ying & Chen, Shaoqing & Xu, Chao, 2016. "Greenhouse gas emission accounting for EU member states from 1991 to 2012," Applied Energy, Elsevier, vol. 184(C), pages 759-768.
    7. Long, Huan & Li, Peikun & Gu, Wei, 2020. "A data-driven evolutionary algorithm for wind farm layout optimization," Energy, Elsevier, vol. 208(C).
    8. Genovaitė Liobikienė & Mindaugas Butkus & Kristina Matuzevičiūtė, 2019. "The Contribution of Energy Taxes to Climate Change Policy in the European Union (EU)," Resources, MDPI, vol. 8(2), pages 1-23, April.
    9. Zhang, Yu & Peng, Zhen & Guan, Yong & Wu, Lifeng, 2021. "Prognostics of battery cycle life in the early-cycle stage based on hybrid model," Energy, Elsevier, vol. 221(C).

    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. 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.
    2. Franki, Vladimir & Višković, Alfredo, 2021. "Multi-criteria decision support: A case study of Southeast Europe power systems," Utilities Policy, Elsevier, vol. 73(C).
    3. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    4. Ismail, M.S. & Moghavvemi, M. & Mahlia, T.M.I., 2013. "Energy trends in Palestinian territories of West Bank and Gaza Strip: Possibilities for reducing the reliance on external energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 117-129.
    5. Daniel Cardoso & Daniel Nunes & João Faria & Paulo Fael & Pedro D. Gaspar, 2023. "Intelligent Micro-Cogeneration Systems for Residential Grids: A Sustainable Solution for Efficient Energy Management," Energies, MDPI, vol. 16(13), pages 1-21, July.
    6. Dujuan Yang & Harry Timmermans & Aloys Borgers, 2016. "The prevalence of context-dependent adjustment of activity-travel patterns in energy conservation strategies: results from a mixture-amount stated adaptation experiment," Transportation, Springer, vol. 43(1), pages 79-100, January.
    7. Jiang-Jiang, Wang & Chun-Fa, Zhang & You-Yin, Jing, 2010. "Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China," Applied Energy, Elsevier, vol. 87(4), pages 1247-1259, April.
    8. 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).
    9. Ciampi, Giovanni & Rosato, Antonio & Sibilio, Sergio, 2018. "Thermo-economic sensitivity analysis by dynamic simulations of a small Italian solar district heating system with a seasonal borehole thermal energy storage," Energy, Elsevier, vol. 143(C), pages 757-771.
    10. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre for Inclusive Growth.
    11. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    12. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    13. Farah Mneimneh & Hasan Ghazzawi & Seeram Ramakrishna, 2023. "Review Study of Energy Efficiency Measures in Favor of Reducing Carbon Footprint of Electricity and Power, Buildings, and Transportation," Circular Economy and Sustainability, Springer, vol. 3(1), pages 447-474, March.
    14. Sibilio, Sergio & Rosato, Antonio & Ciampi, Giovanni & Scorpio, Michelangelo & Akisawa, Atsushi, 2017. "Building-integrated trigeneration system: Energy, environmental and economic dynamic performance assessment for Italian residential applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 920-933.
    15. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    16. Jing, Rui & Wang, Meng & Brandon, Nigel & Zhao, Yingru, 2017. "Multi-criteria evaluation of solid oxide fuel cell based combined cooling heating and power (SOFC-CCHP) applications for public buildings in China," Energy, Elsevier, vol. 141(C), pages 273-289.
    17. Caresana, Flavio & Brandoni, Caterina & Feliciotti, Petro & Bartolini, Carlo Maria, 2011. "Energy and economic analysis of an ICE-based variable speed-operated micro-cogenerator," Applied Energy, Elsevier, vol. 88(3), pages 659-671, March.
    18. Sheykhi, Mohammad & Chahartaghi, Mahmood & Safaei Pirooz, Amir Ali & Flay, Richard G.J., 2020. "Investigation of the effects of operating parameters of an internal combustion engine on the performance and fuel consumption of a CCHP system," Energy, Elsevier, vol. 211(C).
    19. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Working Papers e098, Tokyo Center for Economic Research.
    20. Soo-Jin Lee & You-Jeong Kim & Hye-Sun Jin & Sung-Im Kim & Soo-Yeon Ha & Seung-Yeong Song, 2019. "Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis," Energies, MDPI, vol. 12(12), pages 1-18, June.

    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:84:y:2015:i:c:p:816-824. 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.