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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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    1. Editorial, 2020. "Covid-19 and Climate Change," Journal, Review of Agrarian Studies, vol. 10(1), pages 5-6, January-J.
    2. Rosenberg, Eva & Lind, Arne & Espegren, Kari Aamodt, 2013. "The impact of future energy demand on renewable energy production – Case of Norway," Energy, Elsevier, vol. 61(C), pages 419-431.
    3. Klemeš, Jiří Jaromír & Fan, Yee Van & Jiang, Peng, 2020. "The energy and environmental footprints of COVID-19 fighting measures – PPE, disinfection, supply chains," Energy, Elsevier, vol. 211(C).
    4. Sorrell, Steve, 2015. "Reducing energy demand: A review of issues, challenges and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 74-82.
    5. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    6. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
    7. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(C).
    8. Zi Lin & Xiaolei Liu & Ziming Feng, 2020. "Systematic Investigation of Integrating Small Wind Turbines into Power Supply for Hydrocarbon Production," Energies, MDPI, vol. 13(12), pages 1-16, June.
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    3. 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).
    4. 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.
    5. Bazzana, Davide & Cohen, Jed J. & Golinucci, Nicolò & Hafner, Manfred & Noussan, Michel & Reichl, Johannes & Rocco, Matteo Vincenzo & Sciullo, Alessandro & Vergalli, Sergio, 2022. "A multi-disciplinary approach to estimate the medium-term impact of COVID-19 on transport and energy: A case study for Italy," Energy, Elsevier, vol. 238(PC).
    6. 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.
    7. 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.
    8. Michail Tsangas & Iliana Papamichael & Antonis A. Zorpas, 2023. "Sustainable Energy Planning in a New Situation," Energies, MDPI, vol. 16(4), pages 1-12, February.
    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. 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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