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Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting

Author

Listed:
  • Feras Alasali

    (Department of Electrical Engineering, The Hashemite University, Zarqa 13113, Jordan)

  • Khaled Nusair

    (Protection and Metering Department, National Electric Power Company, Amman 11181, Jordan)

  • Lina Alhmoud

    (Department of Power Engineering, Yarmouk University, Irbid 21163, Jordan)

  • Eyad Zarour

    (Department of Electrical Engineering, Al-Balqa Applied University, Al-Salt 19117, Jordan)

Abstract

The current COVID-19 pandemic and the preventive measures taken to contain the spread of the disease have drastically changed the patterns of our behavior. The pandemic and movement restrictions have significant influences on the behavior of the environment and energy profiles. In 2020, the reliability of the power system became critical under lockdown conditions and the chaining in the electrical consumption behavior. The COVID-19 pandemic will have a long-term effect on the patterns of our behavior. Unlike previous studies that covered only the start of the pandemic period, this paper aimed to examine and analyze electrical demand data over a longer period of time with five years of collected data up until November 2020. In this paper, the demand analysis based on the time series decomposition process is developed through the elimination of the impact of times series correlation, trends, and seasonality on the analysis. This aims to present and only show the pandemic’s impacts on the grid demand. The long-term analysis indicates stress on the grid (half-hourly and daily peaks, baseline demand and demand forecast error) and the effect of the COVID-19 pandemic on the power grid is not a simple reduction in electricity demand. In order to minimize the impact of the pandemic on the performance of the forecasting model, a rolling stochastic Auto Regressive Integrated Moving Average with Exogenous (ARIMAX) model is developed in this paper. The proposed forecast model aims to improve the forecast performance by capturing the non-smooth demand nature through creating a number of future demand scenarios based on a probabilistic model. The proposed forecast model outperformed the benchmark forecast model ARIMAX and Artificial Neural Network (ANN) and reduced the forecast error by up to 23.7%.

Suggested Citation

  • Feras Alasali & Khaled Nusair & Lina Alhmoud & Eyad Zarour, 2021. "Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1435-:d:489674
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    References listed on IDEAS

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    1. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    2. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    3. Janusz Sowinski, 2021. "The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company," Energies, MDPI, vol. 14(2), pages 1-18, January.
    4. Herui Cui & Xu Peng, 2015. "Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-10, July.
    5. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.
    6. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
    8. Khaled Nusair & Feras Alasali, 2020. "Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method," Energies, MDPI, vol. 13(14), pages 1-46, July.
    9. Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
    10. Arpita Samanta Santra & Jun-Lin Lin, 2019. "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(11), pages 1-11, May.
    11. Maher Selim & Ryan Zhou & Wenying Feng & Peter Quinsey, 2021. "Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design," Energies, MDPI, vol. 14(1), pages 1-15, January.
    12. Rae-Jun Park & Kyung-Bin Song & Bo-Sung Kwon, 2020. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
    13. Amevi Acakpovi & Alfred Tettey Ternor & Nana Yaw Asabere & Patrick Adjei & Abdul-Shakud Iddrisu, 2020. "Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, August.
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    Cited by:

    1. Ra ed Nahar Myyas & Mohammad R. Almajali, 2022. "Effect of COVID-19 on Energy Sector in Jordan," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 20-29, March.
    2. 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.
    3. Yasunobu Wakashiro, 2022. "Causal impact of severe events on electricity demand: The case of COVID-19 in Japan," Papers 2206.02122, arXiv.org.
    4. Gang Chen & Qingchang Hu & Jin Wang & Xu Wang & Yuyu Zhu, 2023. "Machine-Learning-Based Electric Power Forecasting," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    5. Smruti Manjunath & Madhura Yeligeti & Maria Fyta & Jannik Haas & Hans-Christian Gils, 2021. "Impact of COVID-19 on Electricity Demand: Deriving Minimum States of System Health for Studies on Resilience," Data, MDPI, vol. 6(7), pages 1-20, July.
    6. Feras Alasali & Mohammad Salameh & Ali Semrin & Khaled Nusair & Naser El-Naily & William Holderbaum, 2022. "Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan," Sustainability, MDPI, vol. 14(13), pages 1-20, July.
    7. 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.
    8. Norman Maswanganyi & Caston Sigauke & Edmore Ranganai, 2021. "Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data," Energies, MDPI, vol. 14(20), pages 1-21, October.
    9. Cristina Hora & Florin Ciprian Dan & Gabriel Bendea & Calin Secui, 2022. "Residential Short-Term Load Forecasting during Atypical Consumption Behavior," Energies, MDPI, vol. 15(1), pages 1-15, January.
    10. Tan Ngoc Dinh & Gokul Sidarth Thirunavukkarasu & Mehdi Seyedmahmoudian & Saad Mekhilef & Alex Stojcevski, 2023. "Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-Series Model with Knowledge Injection," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
    11. Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.
    12. Feras Alasali & Husam Foudeh & Esraa Mousa Ali & Khaled Nusair & William Holderbaum, 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources," Energies, MDPI, vol. 14(8), pages 1-31, April.

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