IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v255y2022ics036054422201413x.html
   My bibliography  Save this article

Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances

Author

Listed:
  • Hamed, Mohammad M.
  • Ali, Hesham
  • Abdelal, Qasem

Abstract

This paper develops heterogeneity-based econometric models that forecast the yearly consumption of electricity. Unlike previous research, this paper departs from classical econometric regression models and explicitly accounts for unobserved heterogeneity and corresponding interactions. More specifically, three modeling approaches are applied: a random parameter linear regression model (RPLRM), a correlated random parameter linear model (C-RPLRM), and a random parameter linear model with heterogeneity in means and variances (RP-HMV). In addition, a grey model is estimated based on historical consumption data. The estimation results clearly demonstrate that the random parameter methodology is statistically superior to the classical multiple linear regression model. Moreover, based on the test results reported in this paper, as well as the forecasting accuracy measures, the RP-HMV model is shown to be statistically outstanding, with a very low forecasting error (MAPE of 0.04%) compared to the other models, including the grey model. Our estimation results show that two variables, namely the average electricity price and contribution of renewable energy to the national and distribution grids, produced statistically significant random parameters with heterogeneous variances and means. Moreover, the results show that factors including the number of households on the distribution grid, average electric power price, and availability of all-season air conditioners with hot and cold inverter characteristics significantly influenced the electricity consumption. These empirical results reveal that random-parameters models with heterogeneity in their means and variances can provide a detailed analysis of the predictor variables shaping the annual electricity consumption, and they highlight the importance of including unobserved heterogeneity and related interactions in econometric models.

Suggested Citation

  • Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:energy:v:255:y:2022:i:c:s036054422201413x
    DOI: 10.1016/j.energy.2022.124510
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422201413X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124510?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. Agustin J. Ros, 2017. "An Econometric Assessment of Electricity Demand in the United States Using Utility-specific Panel Data and the Impact of Retail Competition on Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    2. He, Yongxiu & Jiao, Jie & Chen, Qian & Ge, Sifan & Chang, Yan & Xu, Yang, 2017. "Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin," Energy, Elsevier, vol. 133(C), pages 9-22.
    3. Zahedi, Gholamreza & Azizi, Saeed & Bahadori, Alireza & Elkamel, Ali & Wan Alwi, Sharifah R., 2013. "Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada," Energy, Elsevier, vol. 49(C), pages 323-328.
    4. Kan, Xiaoming & Reichenberg, Lina & Hedenus, Fredrik, 2021. "The impacts of the electricity demand pattern on electricity system cost and the electricity supply mix: A comprehensive modeling analysis for Europe," Energy, Elsevier, vol. 235(C).
    5. Pelay, Ugo & Luo, Lingai & Fan, Yilin & Stitou, Driss & Rood, Mark, 2017. "Thermal energy storage systems for concentrated solar power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 82-100.
    6. Tang, Tao & Jiang, Weiheng & Zhang, Hui & Nie, Jiangtian & Xiong, Zehui & Wu, Xiaogang & Feng, Wenjiang, 2022. "GM(1,1) based improved seasonal index model for monthly electricity consumption forecasting," Energy, Elsevier, vol. 252(C).
    7. Wu, Wen-Ze & Pang, Haodan & Zheng, Chengli & Xie, Wanli & Liu, Chong, 2021. "Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China," Energy, Elsevier, vol. 229(C).
    8. Noel Alter & Shabib Haider Syed, 2011. "An Empirical Analysis of Electricity Demand in Pakistan," International Journal of Energy Economics and Policy, Econjournals, vol. 1(4), pages 116-139.
    9. Jiang, Weiheng & Wu, Xiaogang & Gong, Yi & Yu, Wanxin & Zhong, Xinhui, 2020. "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, Elsevier, vol. 193(C).
    10. Wu, Jianghong & Liu, Chaopeng & Li, Hongqi & Ouyang, Dong & Cheng, Jianhong & Wang, Yuanxia & You, Shaofang, 2017. "Residential air-conditioner usage in China and efficiency standardization," Energy, Elsevier, vol. 119(C), pages 1036-1046.
    11. Lawal, Adedoyin Isola & Ozturk, Ilhan & Olanipekun, Ifedolapo O. & Asaleye, Abiola John, 2020. "Examining the linkages between electricity consumption and economic growth in African economies," Energy, Elsevier, vol. 208(C).
    12. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    13. Koščak Kolin, Sonja & Karasalihović Sedlar, Daria & Kurevija, Tomislav, 2021. "Relationship between electricity and economic growth for long-term periods: New possibilities for energy prediction," Energy, Elsevier, vol. 228(C).
    14. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    15. Erdogdu, Erkan, 2007. "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, Elsevier, vol. 35(2), pages 1129-1146, February.
    16. Yilmaz, S. & Majcen, D. & Heidari, M. & Mahmoodi, J. & Brosch, T. & Patel, M.K., 2019. "Analysis of the impact of energy efficiency labelling and potential changes on electricity demand reduction of white goods using a stock model: The case of Switzerland," Applied Energy, Elsevier, vol. 239(C), pages 117-132.
    17. Tang, Lei & Wang, Xifan & Wang, Xiuli & Shao, Chengcheng & Liu, Shiyu & Tian, Shijun, 2019. "Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory," Energy, Elsevier, vol. 167(C), pages 1144-1154.
    18. De Vita, G. & Endresen, K. & Hunt, L.C., 2006. "An empirical analysis of energy demand in Namibia," Energy Policy, Elsevier, vol. 34(18), pages 3447-3463, December.
    19. Yi-Tui Chen, 2017. "The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    20. Athukorala, P.P.A Wasantha & Wilson, Clevo, 2010. "Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka," Energy Economics, Elsevier, vol. 32(Supplemen), pages 34-40, September.
    21. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    22. Athukorala, P.P.A Wasantha & Wilson, Clevo, 2010. "Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka," Energy Economics, Elsevier, vol. 32(Supplemen), pages 34-40, September.
    23. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    24. Sheng, Pengfei & Guo, Xiaohui, 2018. "Energy consumption associated with urbanization in China: Efficient- and inefficient-use," Energy, Elsevier, vol. 165(PB), pages 118-125.
    25. Alva, Guruprasad & Liu, Lingkun & Huang, Xiang & Fang, Guiyin, 2017. "Thermal energy storage materials and systems for solar energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 693-706.
    26. Chen, Hai-Bao & Pei, Ling-Ling & Zhao, Yu-Feng, 2021. "Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach," Energy, Elsevier, vol. 222(C).
    27. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    28. Boßmann, T. & Staffell, I., 2015. "The shape of future electricity demand: Exploring load curves in 2050s Germany and Britain," Energy, Elsevier, vol. 90(P2), pages 1317-1333.
    29. Spees, Kathleen & Lave, Lester B., 2007. "Demand Response and Electricity Market Efficiency," The Electricity Journal, Elsevier, vol. 20(3), pages 69-85, April.
    30. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
    31. Kwon, Sanguk & Cho, Seong-Hoon & Roberts, Roland K. & Kim, Hyun Jae & Park, Kihyun & Edward Yu, T., 2016. "Effects of electricity-price policy on electricity demand and manufacturing output," Energy, Elsevier, vol. 102(C), pages 324-334.
    32. Jacobsen, Grant D., 2015. "Do energy prices influence investment in energy efficiency? Evidence from energy star appliances," Journal of Environmental Economics and Management, Elsevier, vol. 74(C), pages 94-106.
    33. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.
    34. Hou, Rui & Li, Shanshan & Wu, Minrong & Ren, Guowen & Gao, Wei & Khayatnezhad, Majid & gholinia, Fatemeh, 2021. "Assessing of impact climate parameters on the gap between hydropower supply and electricity demand by RCPs scenarios and optimized ANN by the improved Pathfinder (IPF) algorithm," Energy, Elsevier, vol. 237(C).
    35. Atalla, Tarek N. & Hunt, Lester C., 2016. "Modelling residential electricity demand in the GCC countries," Energy Economics, Elsevier, vol. 59(C), pages 149-158.
    36. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).
    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. Issouf Fofana & Stephan Brettschneider, 2022. "Outdoor Insulation and Gas-Insulated Switchgears," Energies, MDPI, vol. 15(21), pages 1-7, November.
    2. Hamed, Mohammad M. & Mohammed, Ali & Olabi, Abdul Ghani, 2023. "Renewable energy adoption decisions in Jordan's industrial sector: Statistical analysis with unobserved heterogeneity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.

    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. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    2. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    3. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.
    4. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    5. Tiwari, Aviral Kumar & Menegaki, Angeliki N., 2019. "A time varying approach on the price elasticity of electricity in India during 1975–2013," Energy, Elsevier, vol. 183(C), pages 385-397.
    6. Al-Bajjali, Saif Kayed & Shamayleh, Adel Yacoub, 2018. "Estimating the determinants of electricity consumption in Jordan," Energy, Elsevier, vol. 147(C), pages 1311-1320.
    7. Masike, Kabelo & Vermeulen, Cobus, 2022. "The time-varying elasticity of South African electricity demand," Energy, Elsevier, vol. 238(PC).
    8. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
    9. Paul Adjei Kwakwa & Solomon Aboagye, 2014. "Energy consumption in Ghana and the story of economic growth, industrialization, trade openness and urbanization," Asian Bulletin of Energy Economics and Technology, Asian Online Journal Publishing Group, vol. 1(1), pages 1-6.
    10. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    11. Aisha Kolawole & Sola Adesola & Glauco De Vita, 2017. "A Disaggregated Analysis of Energy Demand in Sub-Saharan Africa," International Journal of Energy Economics and Policy, Econjournals, vol. 7(2), pages 224-235.
    12. Adom, Philip Kofi & Bekoe, William & Akoena, Sesi Kutri Komla, 2012. "Modelling aggregate domestic electricity demand in Ghana: An autoregressive distributed lag bounds cointegration approach," Energy Policy, Elsevier, vol. 42(C), pages 530-537.
    13. Carlos Enrique Carrasco-Gutierrez & Philipp Ehrl, 2023. "Regional Estimates of Residential Electricity Demand in Brazil," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 465-476, January.
    14. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio & Minea, Alina A., 2010. "Analysis and forecasting of nonresidential electricity consumption in Romania," Applied Energy, Elsevier, vol. 87(11), pages 3584-3590, November.
    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. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    17. Günay, M. Erdem, 2016. "Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey," Energy Policy, Elsevier, vol. 90(C), pages 92-101.
    18. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
    19. repec:eco:journ2:2017-04-33 is not listed on IDEAS
    20. Haitao Yin & Hui Zhou & Kai Zhu, 2016. "Long- and short-run elasticities of residential electricity consumption in China: a partial adjustment model with panel data," Applied Economics, Taylor & Francis Journals, vol. 48(28), pages 2587-2599, June.
    21. 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.

    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:255:y:2022:i:c:s036054422201413x. 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.