IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v237y2025icp344-354.html

Impact of COVID-19 lockdown in short-term load forecasting

Author

Listed:
  • López, Miguel
  • Valero, Sergio
  • Senabre, Carolina

Abstract

Accurate prediction of electrical demand is crucial for the efficient operation of power systems. However, the unprecedented activity restrictions imposed during the pandemic led to unforeseen disruptions in electrical consumption, challenging the predictive capabilities of existing systems. This phenomenon was widespread, affecting power systems globally, as evidenced by analyses of the Spanish electricity grid presented in this paper. The precision of prediction systems significantly diminished upon the implementation of activity restrictions. This article offers an in-depth analysis of the impact on prediction accuracy in the Spanish context. Moreover, it proposes a method to identify situations where the prediction system is out of control, necessitating corrective measures. The paper introduces a straightforward corrective measure that reduces errors during out-of-control periods. It is designed as an addition to the existing forecast model, modifying its output when an out-of-control situation is reached. The results suggest that further investigation could substantially mitigate the impact of such events and enhance prediction system resilience.

Suggested Citation

  • López, Miguel & Valero, Sergio & Senabre, Carolina, 2025. "Impact of COVID-19 lockdown in short-term load forecasting," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 237(C), pages 344-354.
    • Handle: RePEc:eee:matcom:v:237:y:2025:i:c:p:344-354
    DOI: 10.1016/j.matcom.2025.04.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475425001739
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2025.04.035?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Xiaolei & Lin, Zi, 2021. "Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory," Energy, Elsevier, vol. 227(C).
    2. Miguel López & Carlos Sans & Sergio Valero & Carolina Senabre, 2019. "Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study," Energies, MDPI, vol. 12(7), pages 1-31, April.
    3. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac & Valentin A. Boicea, 2021. "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study," Energies, MDPI, vol. 14(13), pages 1-19, July.
    4. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
    5. Liu, Jiefeng & Zhang, Zhenhao & Fan, Xianhao & Zhang, Yiyi & Wang, Jiaqi & Zhou, Ke & Liang, Shuo & Yu, Xiaoyong & Zhang, Wei, 2022. "Power system load forecasting using mobility optimization and multi-task learning in COVID-19," Applied Energy, Elsevier, vol. 310(C).
    6. Miguel López & Sergio Valero & Carlos Sans & Carolina Senabre, 2020. "Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy," Energies, MDPI, vol. 14(1), pages 1-14, December.
    7. Santiago, I. & Moreno-Munoz, A. & Quintero-Jiménez, P. & Garcia-Torres, F. & Gonzalez-Redondo, M.J., 2021. "Electricity demand during pandemic times: The case of the COVID-19 in Spain," Energy Policy, Elsevier, vol. 148(PA).
    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. Quintero Gutiérrez, Laura Victoria & García Rendón, John & Gutiérrez Gómez, Alejandro, 2024. "Impact of COVID-19 preventive measures on electricity demand: Evidence from Colombia," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    2. Zhiang Zhang & Ali Cheshmehzangi & Saeid Pourroostaei Ardakani, 2021. "A Data-Driven Clustering Analysis for the Impact of COVID-19 on the Electricity Consumption Pattern of Zhejiang Province, China," Energies, MDPI, vol. 14(23), pages 1-22, December.
    3. Halbrügge, Stephanie & Buhl, Hans Ulrich & Fridgen, Gilbert & Schott, Paul & Weibelzahl, Martin & Weissflog, Jan, 2022. "How Germany achieved a record share of renewables during the COVID-19 pandemic while relying on the European interconnected power network," Energy, Elsevier, vol. 246(C).
    4. Garcia-Rendon, John & Rey Londoño, Felipe & Arango Restrepo, Luis José & Bohorquez Correa, Santiago, 2023. "Sectoral analysis of electricity consumption during the COVID-19 pandemic: Evidence for unregulated and regulated markets in Colombia," Energy, Elsevier, vol. 268(C).
    5. Davi-Arderius, Daniel & Schittekatte, Tim, 2023. "Carbon emissions impacts of operational network constraints: The case of Spain during the Covid-19 crisis," Energy Economics, Elsevier, vol. 128(C).
    6. Yukseltan, E. & Kok, A. & Yucekaya, A. & Bilge, A. & Aktunc, E. Agca & Hekimoglu, M., 2022. "The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey," Utilities Policy, Elsevier, vol. 76(C).
    7. Wang, Kai-Hua & Su, Chi-Wei & Lobonţ, Oana-Ramona & Umar, Muhammad, 2021. "Whether crude oil dependence and CO2 emissions influence military expenditure in net oil importing countries?," Energy Policy, Elsevier, vol. 153(C).
    8. Dong, Hanjiang & Zhu, Jizhong & Li, Shenglin & Wu, Wanli & Zhu, Haohao & Fan, Junwei, 2023. "Short-term residential household reactive power forecasting considering active power demand via deep Transformer sequence-to-sequence networks," Applied Energy, Elsevier, vol. 329(C).
    9. Cassetti, Gabriele & Boitier, Baptiste & Elia, Alessia & Le Mouël, Pierre & Gargiulo, Maurizio & Zagamé, Paul & Nikas, Alexandros & Koasidis, Konstantinos & Doukas, Haris & Chiodi, Alessandro, 2023. "The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: An energy-economic modelling perspective," Energy, Elsevier, vol. 263(PC).
    10. Teresa Cuerdo-Vilches & Miguel Ángel Navas-Martín & Ignacio Oteiza, 2021. "Behavior Patterns, Energy Consumption and Comfort during COVID-19 Lockdown Related to Home Features, Socioeconomic Factors and Energy Poverty in Madrid," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    11. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    12. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    13. Smirnov, Dmitry & Huchzermeier, Arnd, 2020. "Analytics for labor planning in systems with load-dependent service times," European Journal of Operational Research, Elsevier, vol. 287(2), pages 668-681.
    14. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
    15. Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    16. Hu, Wenxuan & Scholz, Yvonne & Yeligeti, Madhura & Deng, Ying & Jochem, Patrick, 2024. "Future electricity demand for Europe: Unraveling the dynamics of the Temperature Response Function," Applied Energy, Elsevier, vol. 368(C).
    17. Aguirre, Gerson & Lazo, Joaquín & Watts, David, 2023. "Regulatory response to COVID-19 in the Ibero-American electricity sector: Policy implications and critical analysis of support measures," Energy Policy, Elsevier, vol. 183(C).
    18. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
    19. Yang, Chunmeng & Bu, Siqi & Fan, Yi & Wan, Wayne Xinwei & Wang, Ruoheng & Foley, Aoife, 2023. "Data-driven prediction and evaluation on future impact of energy transition policies in smart regions," Applied Energy, Elsevier, vol. 332(C).
    20. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:matcom:v:237:y:2025:i:c:p:344-354. 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/mathematics-and-computers-in-simulation/ .

    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.