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Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory

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  • Liu, Xiaolei
  • Lin, Zi

Abstract

Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach.

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  • 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).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007040
    DOI: 10.1016/j.energy.2021.120455
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    5. 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).
    6. 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).
    7. Michail Tsangas & Iliana Papamichael & Antonis A. Zorpas, 2023. "Sustainable Energy Planning in a New Situation," Energies, MDPI, vol. 16(4), pages 1-12, February.
    8. 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).
    9. Fahad Radhi Alharbi & Denes Csala, 2021. "Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks," Energies, MDPI, vol. 14(20), pages 1-22, October.
    10. 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.
    11. 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).
    12. Trizoglou, Pavlos & Liu, Xiaolei & Lin, Zi, 2021. "Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines," Renewable Energy, Elsevier, vol. 179(C), pages 945-962.
    13. 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).

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