IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i19p5171-d423823.html
   My bibliography  Save this article

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

Author

Listed:
  • Seyed Azad Nabavi

    (Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran 16589-53571, Iran)

  • Alireza Aslani

    (Department of Renewable Energy and Environment, University of Tehran, Tehran 14174-66191, Iran)

  • Martha A. Zaidan

    (Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, FI-00014 Helsinki, Finland)

  • Majid Zandi

    (Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran 16589-53571, Iran)

  • Sahar Mohammadi

    (Department of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran 16589-53571, Iran)

  • Naser Hossein Motlagh

    (Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland)

Abstract

Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption.

Suggested Citation

  • Seyed Azad Nabavi & Alireza Aslani & Martha A. Zaidan & Majid Zandi & Sahar Mohammadi & Naser Hossein Motlagh, 2020. "Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors," Energies, MDPI, vol. 13(19), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5171-:d:423823
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/19/5171/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/19/5171/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Samadi, Seyed Hashem & Ghobadian, Barat & Nosrati, Mohsen, 2020. "Prediction and estimation of biomass energy from agricultural residues using air gasification technology in Iran," Renewable Energy, Elsevier, vol. 149(C), pages 1077-1091.
    2. Farzan, Farbod & Jafari, Mohsen A. & Gong, Jie & Farzan, Farnaz & Stryker, Andrew, 2015. "A multi-scale adaptive model of residential energy demand," Applied Energy, Elsevier, vol. 150(C), pages 258-273.
    3. Hall, Lisa M.H. & Buckley, Alastair R., 2016. "A review of energy systems models in the UK: Prevalent usage and categorisation," Applied Energy, Elsevier, vol. 169(C), pages 607-628.
    4. 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.
    5. Jihoon Min & Zeke Hausfather & Qi Feng Lin, 2010. "A High‐Resolution Statistical Model of Residential Energy End Use Characteristics for the United States," Journal of Industrial Ecology, Yale University, vol. 14(5), pages 791-807, October.
    6. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    7. Hafeznia, Hamed & Pourfayaz, Fathollah & Maleki, Akbar, 2017. "An assessment of Iran's natural gas potential for transition toward low-carbon economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 71-81.
    8. Dehghan Shabani, Zahra & Shahnazi, Rouhollah, 2019. "Energy consumption, carbon dioxide emissions, information and communications technology, and gross domestic product in Iranian economic sectors: A panel causality analysis," Energy, Elsevier, vol. 169(C), pages 1064-1078.
    9. Brandt, Adam R. & Dale, Michael & Barnhart, Charles J., 2013. "Calculating systems-scale energy efficiency and net energy returns: A bottom-up matrix-based approach," Energy, Elsevier, vol. 62(C), pages 235-247.
    10. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    11. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    12. Forouzanfar, Mehdi & Doustmohammadi, Ali & Menhaj, M. Bagher & Hasanzadeh, Samira, 2010. "Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran," Applied Energy, Elsevier, vol. 87(1), pages 268-274, January.
    13. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    14. Siller, Thomas & Kost, Michael & Imboden, Dieter, 2007. "Long-term energy savings and greenhouse gas emission reductions in the Swiss residential sector," Energy Policy, Elsevier, vol. 35(1), pages 529-539, January.
    15. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    16. Dai, Hancheng & Mischke, Peggy & Xie, Xuxuan & Xie, Yang & Masui, Toshihiko, 2016. "Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions," Applied Energy, Elsevier, vol. 162(C), pages 1355-1373.
    17. 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.
    18. Khuram Pervez Amber & Muhammad Waqar Aslam & Anzar Mahmood & Anila Kousar & Muhammad Yamin Younis & Bilal Akbar & Ghulam Qadar Chaudhary & Syed Kashif Hussain, 2017. "Energy Consumption Forecasting for University Sector Buildings," Energies, MDPI, vol. 10(10), pages 1-18, October.
    19. 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.
    20. 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.
    21. Hosseini, Seyed Ehsan & Andwari, Amin Mahmoudzadeh & Wahid, Mazlan Abdul & Bagheri, Ghobad, 2013. "A review on green energy potentials in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 533-545.
    22. Nejat, Payam & Jomehzadeh, Fatemeh & Taheri, Mohammad Mahdi & Gohari, Mohammad & Abd. Majid, Muhd Zaimi, 2015. "A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 843-862.
    23. Asghar Shahmoradi & Hamed Shakouri, 2010. "Investigation on the Impact of an Energy Desubsidization Shock on the General Price Index Via a Nonlinear Inflation Model: Case of Iran," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 15(3), pages 33-51, fall.
    24. Mohammadnejad, M. & Ghazvini, M. & Mahlia, T.M.I. & Andriyana, A., 2011. "A review on energy scenario and sustainable energy in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4652-4658.
    25. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    26. Gorjian, Shiva & Zadeh, Babak Nemat & Eltrop, Ludger & Shamshiri, Redmond R. & Amanlou, Yasaman, 2019. "Solar photovoltaic power generation in Iran: Development, policies, and barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 106(C), pages 110-123.
    27. Gharehgozli, Orkideh, 2017. "An estimation of the economic cost of recent sanctions on Iran using the synthetic control method," Economics Letters, Elsevier, vol. 157(C), pages 141-144.
    28. 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.
    29. Cayla, Jean-Michel & Maizi, Nadia & Marchand, Christophe, 2011. "The role of income in energy consumption behaviour: Evidence from French households data," Energy Policy, Elsevier, vol. 39(12), pages 7874-7883.
    30. Zanjirchi, Seyed Mahmoud & Shojaei, Sara & Naser Sadrabadi, Alireza & Jalilian, Negar, 2020. "Promotion of solar energies usage in Iran: A scenario-based road map," Renewable Energy, Elsevier, vol. 150(C), pages 278-292.
    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. Naser Hossein Motlagh & Ali Khatibi & Alireza Aslani, 2020. "Toward Sustainable Energy-Independent Buildings Using Internet of Things," Energies, MDPI, vol. 13(22), pages 1-17, November.
    2. 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).

    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. 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).
    2. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    3. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    4. 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.
    5. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    6. Kwok Wai Mui & Ling Tim Wong & Manoj Kumar Satheesan & Anjana Balachandran, 2021. "A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong," Energies, MDPI, vol. 14(16), pages 1-18, August.
    7. 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.
    8. 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.
    9. 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.
    10. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    11. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    12. R. Rueda & M. P. Cuéllar & M. Molina-Solana & Y. Guo & M. C. Pegalajar, 2019. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting," Energies, MDPI, vol. 12(6), pages 1-22, March.
    13. 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.
    14. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    15. 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.
    16. 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).
    17. Rahim Zahedi & Alireza Zahedi & Abolfazl Ahmadi, 2022. "Strategic Study for Renewable Energy Policy, Optimizations and Sustainability in Iran," Sustainability, MDPI, vol. 14(4), pages 1-29, February.
    18. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    19. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    20. Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(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:gam:jeners:v:13:y:2020:i:19:p:5171-:d:423823. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.