IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9316-d875361.html
   My bibliography  Save this article

Impact of COVID-19 Pandemic on Qatar Electricity Demand and Load Forecasting: Preparedness of Distribution Networks for Emerging Situations

Author

Listed:
  • Omar Jouma El-Hafez

    (IPP Contracts and Agreements Engineer, Qatar General Electricity and Water Corporation (KAHRAMAA), Doha P.O. Box 41, Qatar)

  • Tarek Y. ElMekkawy

    (Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

  • Mohamed Kharbeche

    (Qatar Transportation and Traffic Safety Center, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

  • Ahmed Massoud

    (Department of Electrical Engineering, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

Abstract

The COVID-19 pandemic has brought several global challenges, one of which is meeting the electricity demand. Millions of people are confined to their homes, in each of which a reliable electricity supply is needed, to support teleworking, e-commerce, and electrical appliances such as HVAC, lighting, fridges, water heaters, etc. Furthermore, electricity is also required to operate medical equipment in hospitals and perhaps temporary quarantine hospitals/shelters. Electricity demand forecasting is a crucial input into decision-making for electricity providers. Without an accurate forecast of electricity demand, over-capacity or shortages in the power supply may result in high costs, network bottlenecks, and instability. Electricity demand can be divided, typically, into two sectors: domestic and industrial. This paper discusses the impact of the COVID 19 pandemic on Qatar’s electricity demand and forecasting. It is noted that students’ and employees’ attendance are the restrictions with the highest impact on electricity demand. There was an increase of nearly 28% in the domestic peak due to the attendance of 30% of school students. Furthermore, in this study, historical data on Qatar’s electricity demand, population, and GDP were collected, along with information on COVID-19 restrictions. Statistical analysis was used to unfold the impact of the COVID-19 pandemic. The results and findings will help decision-makers and planners manage future electricity demand, and support distribution networks’ preparedness for emerging situations.

Suggested Citation

  • Omar Jouma El-Hafez & Tarek Y. ElMekkawy & Mohamed Kharbeche & Ahmed Massoud, 2022. "Impact of COVID-19 Pandemic on Qatar Electricity Demand and Load Forecasting: Preparedness of Distribution Networks for Emerging Situations," Sustainability, MDPI, vol. 14(15), pages 1-13, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9316-:d:875361
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9316/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/15/9316/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    2. Li, Z. & Hurn, A.S. & Clements, A.E., 2017. "Forecasting quantiles of day-ahead electricity load," Energy Economics, Elsevier, vol. 67(C), pages 60-71.
    3. Marcin Malec & Grzegorz Kinelski & Marzena Czarnecka, 2021. "The Impact of COVID-19 on Electricity Demand Profiles: A Case Study of Selected Business Clients in Poland," Energies, MDPI, vol. 14(17), pages 1-17, August.
    4. Inglesi, Roula, 2010. "Aggregate electricity demand in South Africa: Conditional forecasts to 2030," Applied Energy, Elsevier, vol. 87(1), pages 197-204, January.
    5. Xiaoyu Lin & Hang Yu & Meng Wang & Chaoen Li & Zi Wang & Yin Tang, 2021. "Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method," Energies, MDPI, vol. 14(16), pages 1-21, August.
    6. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
    7. Al-omary, Murad & Kaltschmitt, Martin & Becker, Christian, 2018. "Electricity system in Jordan: Status & prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2398-2409.
    8. Marwen Elkamel & Lily Schleider & Eduardo L. Pasiliao & Ali Diabat & Qipeng P. Zheng, 2020. "Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study," Energies, MDPI, vol. 13(15), pages 1-21, August.
    9. Muley, Deepti & Ghanim, Mohammad Shareef & Mohammad, Anas & Kharbeche, Mohamed, 2021. "Quantifying the impact of COVID–19 preventive measures on traffic in the State of Qatar," Transport Policy, Elsevier, vol. 103(C), pages 45-59.
    10. Yeqi An & Yulin Zhou & Rongrong Li, 2019. "Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods," Energies, MDPI, vol. 12(13), pages 1-24, July.
    Full references (including those not matched with items on IDEAS)

    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. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    2. Yi Yang & Zhihao Shang & Yao Chen & Yanhua Chen, 2020. "Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting," Energies, MDPI, vol. 13(3), pages 1-19, January.
    3. Richard Bean, 2023. "Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge," Energies, MDPI, vol. 16(3), pages 1-23, January.
    4. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    5. Bożena Gajdzik & Magdalena Jaciow & Radosław Wolniak & Robert Wolny & Wieslaw Wes Grebski, 2023. "Assessment of Energy and Heat Consumption Trends and Forecasting in the Small Consumer Sector in Poland Based on Historical Data," Resources, MDPI, vol. 12(9), pages 1-33, September.
    6. Adom, Philip Kofi & Bekoe, William, 2012. "Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: A comparison of ARDL and PAM," Energy, Elsevier, vol. 44(1), pages 367-380.
    7. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    8. Morgan Bazilian & Patrick Nussbaumer & Hans-Holger Rogner & Abeeku Brew-Hammond & Vivien Foster & Shonali Pachauri & Eric Williams & Mark Howells & Philippe Niyongabo & Lawrence Musaba & Brian Ó Galla, 2011. "Energy Access Scenarios to 2030 for the Power Sector in Sub-Saharan Africa," Working Papers 2011.68, Fondazione Eni Enrico Mattei.
    9. Andoni, Merlinda & Robu, Valentin & Flynn, David & Abram, Simone & Geach, Dale & Jenkins, David & McCallum, Peter & Peacock, Andrew, 2019. "Blockchain technology in the energy sector: A systematic review of challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 143-174.
    10. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    11. Norbert Bajkó & Zsolt Fülöp & Kinga Nagyné Pércsi, 2022. "Changes in the Innovation- and Marketing-Habits of Family SMEs in the Foodstuffs Industry, Caused by the Coronavirus Pandemic in Hungary," Sustainability, MDPI, vol. 14(5), pages 1-17, March.
    12. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    13. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    14. Baruah, Debendra Chandra & Enweremadu, Christopher Chintua, 2019. "Prospects of decentralized renewable energy to improve energy access: A resource-inventory-based analysis of South Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 328-341.
    15. Grzegorz Kinelski & Jakub Stęchły & Piotr Bartkowiak, 2022. "Various Facets of Sustainable Smart City Management: Selected Examples from Polish Metropolitan Areas," Energies, MDPI, vol. 15(9), pages 1-23, April.
    16. Shujaat Abbas & Hazrat Yousaf & Shabeer Khan & Mohd Ziaur Rehman & Dmitri Blueschke, 2023. "Analysis and Projection of Transport Sector Demand for Energy and Carbon Emission: An Application of the Grey Model in Pakistan," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    17. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    18. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
    19. Shuai Yu & Bin Li & Dongmei Liu, 2023. "Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing–Tianjin–Hebei Urban Agglomera," IJERPH, MDPI, vol. 20(2), pages 1-22, January.
    20. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.

    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:jsusta:v:14:y:2022:i:15:p:9316-:d:875361. 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.