IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v302y2021ics0306261921009181.html
   My bibliography  Save this article

Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods

Author

Listed:
  • Şahin, Utkucan
  • Ballı, Serkan
  • Chen, Yan

Abstract

Balances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%–25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector.

Suggested Citation

  • Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
  • Handle: RePEc:eee:appene:v:302:y:2021:i:c:s0306261921009181
    DOI: 10.1016/j.apenergy.2021.117540
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921009181
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2021.117540?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
    2. Rouleau, Jean & Gosselin, Louis, 2021. "Impacts of the COVID-19 lockdown on energy consumption in a Canadian social housing building," Applied Energy, Elsevier, vol. 287(C).
    3. Sui, Yi & Zhang, Haoran & Shang, Wenlong & Sun, Rencheng & Wang, Changying & Ji, Jun & Song, Xuan & Shao, Fengjing, 2020. "Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future," Applied Energy, Elsevier, vol. 280(C).
    4. Jiang, Peng & Fan, Yee Van & Klemeš, Jiří Jaromír, 2021. "Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities," Applied Energy, Elsevier, vol. 285(C).
    5. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    6. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    7. Halbrügge, Stephanie & Schott, Paul & Weibelzahl, Martin & Buhl, Hans Ulrich & Fridgen, Gilbert & Schöpf, Michael, 2021. "How did the German and other European electricity systems react to the COVID-19 pandemic?," Applied Energy, Elsevier, vol. 285(C).
    8. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    9. Li, Nu & Wang, Jianliang & Wu, Lifeng & Bentley, Yongmei, 2021. "Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization," Energy, Elsevier, vol. 215(PA).
    10. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Namje Park, 2020. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting," Energies, MDPI, vol. 13(11), pages 1-23, May.
    11. García, Sebastián & Parejo, Antonio & Personal, Enrique & Ignacio Guerrero, Juan & Biscarri, Félix & León, Carlos, 2021. "A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level," Applied Energy, Elsevier, vol. 287(C).
    12. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    13. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
    14. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    15. Yang, Yang & Xue, Dingyü, 2016. "Continuous fractional-order grey model and electricity prediction research based on the observation error feedback," Energy, Elsevier, vol. 115(P1), pages 722-733.
    16. 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).
    17. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    18. López Prol, Javier & O, Sungmin, 2020. "Impact of COVID-19 measures on electricity consumption," MPRA Paper 101649, University Library of Munich, Germany.
    19. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    20. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    21. Chen, Chun-I, 2008. "Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 278-287.
    22. Chen, Hai-Bao & Pei, Ling-Ling & Zhao, Yu-Feng, 2021. "Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach," Energy, Elsevier, vol. 222(C).
    23. Werth, Annette & Gravino, Pietro & Prevedello, Giulio, 2021. "Impact analysis of COVID-19 responses on energy grid dynamics in Europe," Applied Energy, Elsevier, vol. 281(C).
    24. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    25. Meng Dun & Zhicun Xu & Yan Chen & Lifeng Wu, 2020. "Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, May.
    26. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    27. Jianwei Mi & Libin Fan & Xuechao Duan & Yuanying Qiu, 2018. "Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, March.
    28. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    29. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    30. Zhang, Haoran & Yan, Jinyue & Yu, Qing & Obersteiner, Michael & Li, Wenjing & Chen, Jinyu & Zhang, Qiong & Jiang, Mingkun & Wallin, Fredrik & Song, Xuan & Wu, Jiang & Wang, Xin & Shibasaki, Ryosuke, 2021. "1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown," Applied Energy, Elsevier, vol. 283(C).
    31. Madurai Elavarasan, Rajvikram & Shafiullah, GM & Raju, Kannadasan & Mudgal, Vijay & Arif, M.T. & Jamal, Taskin & Subramanian, Senthilkumar & Sriraja Balaguru, V.S. & Reddy, K.S. & Subramaniam, Umashan, 2020. "COVID-19: Impact analysis and recommendations for power sector operation," Applied Energy, Elsevier, vol. 279(C).
    32. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    33. Xie, Wanli & Wu, Wen-Ze & Liu, Chong & Zhao, Jingjie, 2020. "Forecasting annual electricity consumption in China by employing a conformable fractional grey model in opposite direction," Energy, Elsevier, vol. 202(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Huimin & Dang, Yaoguo & Yang, Yingjie & Wang, Junjie & Yang, Shaowen, 2023. "An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles," Energy, Elsevier, vol. 263(PC).
    2. Carlo Andrea Bollino & Maria Chiara D’Errico, 2022. "Electricity Demand Elasticity, Mobility, and COVID-19 Contagion Nexus in the Italian Day-Ahead Electricity Market," Energies, MDPI, vol. 15(20), pages 1-26, October.
    3. Xiong, Xin & Hu, Xi & Tian, Tian & Guo, Huan & Liao, Han, 2022. "A novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index model for hydropower generation," Applied Energy, Elsevier, vol. 328(C).
    4. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    5. Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).
    6. Weijie Zhou & Huihui Tao & Jiaxin Chang & Huimin Jiang & Li Chen, 2023. "Forecasting Chinese Electricity Consumption Based on Grey Seasonal Model with New Information Priority," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    7. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
    8. Lao, Tongfei & Sun, Yanrui, 2022. "Predicting the production and consumption of natural gas in China by using a new grey forecasting method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 295-315.
    9. Ye, Li & Dang, Yaoguo & Fang, Liping & Wang, Junjie, 2023. "A nonlinear interactive grey multivariable model based on dynamic compensation for forecasting the economy-energy-environment system," Applied Energy, Elsevier, vol. 331(C).
    10. Li, Yi & Liu, Tianya & Xu, Jinpeng, 2023. "Analyzing the economic, social, and technological determinants of renewable and nonrenewable electricity production in China: Findings from time series models," Energy, Elsevier, vol. 282(C).
    11. Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
    12. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
    13. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
    14. Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
    15. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    16. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).

    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. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
    2. Costa, Vinicius B.F. & Pereira, Lígia C. & Andrade, Jorge V.B. & Bonatto, Benedito D., 2022. "Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model," Applied Energy, Elsevier, vol. 313(C).
    3. 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.
    4. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    5. Georgeta Soava & Anca Mehedintu & Mihaela Sterpu & Eugenia Grecu, 2021. "The Impact of the COVID-19 Pandemic on Electricity Consumption and Economic Growth in Romania," Energies, MDPI, vol. 14(9), pages 1-25, April.
    6. Song, Zhe & Liu, Jia & Yang, Hongxing, 2021. "Air pollution and soiling implications for solar photovoltaic power generation: A comprehensive review," Applied Energy, Elsevier, vol. 298(C).
    7. Cerqueira, Pedro André & Pereira da Silva, Patrícia, 2023. "Assessment of the impact of COVID-19 lockdown measures on electricity consumption – Evidence from Portugal and Spain," Energy, Elsevier, vol. 282(C).
    8. Lazo, Joaquín & Aguirre, Gerson & Watts, David, 2022. "An impact study of COVID-19 on the electricity sector: A comprehensive literature review and Ibero-American survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    9. 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.
    10. He, Jing & Mao, Shuhua & Kang, Yuxiao, 2023. "Augmented fractional accumulation grey model and its application: Class ratio and restore error perspectives," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 220-247.
    11. M. A. Hannan & M. S. Abd Rahman & Ali Q. Al-Shetwi & R. A. Begum & Pin Jern Ker & M. Mansor & M. S. Mia & M. J. Hossain & Z. Y. Dong & T. M. I. Mahlia, 2022. "Impact Assessment of COVID-19 Severity on Environment, Economy and Society towards Affecting Sustainable Development Goals," Sustainability, MDPI, vol. 14(23), pages 1-23, November.
    12. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    13. Ai, Hongshan & Zhong, Tenglong & Zhou, Zhengqing, 2022. "The real economic costs of COVID-19: Insights from electricity consumption data in Hunan Province, China," Energy Economics, Elsevier, vol. 105(C).
    14. Hoang, Anh Tuan & Sandro Nižetić, & Olcer, Aykut I. & Ong, Hwai Chyuan & Chen, Wei-Hsin & Chong, Cheng Tung & Thomas, Sabu & Bandh, Suhaib A. & Nguyen, Xuan Phuong, 2021. "Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: Opportunities, challenges, and policy implications," Energy Policy, Elsevier, vol. 154(C).
    15. Wu, Wen-Ze & Pang, Haodan & Zheng, Chengli & Xie, Wanli & Liu, Chong, 2021. "Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China," Energy, Elsevier, vol. 229(C).
    16. Micheli, Leonardo & Solas, Álvaro F. & Soria-Moya, Alberto & Almonacid, Florencia & Fernandez, Eduardo F., 2021. "Short-Term Impact of the COVID-19 Lockdown on the Energy and Economic Performance of Photovoltaics in the Spanish Electricity Sector," MPRA Paper 107969, University Library of Munich, Germany.
    17. Abulibdeh, Ammar, 2021. "Spatiotemporal analysis of water-electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors in Doha City, Qatar," Applied Energy, Elsevier, vol. 304(C).
    18. Tomasz Cieślik & Piotr Narloch & Adam Szurlej & Krzysztof Kogut, 2022. "Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland," Energies, MDPI, vol. 15(4), pages 1-18, February.
    19. Fatin Samara & Bassam A. Abu-Nabah & Waleed El-Damaty & Mayyada Al Bardan, 2022. "Assessment of the Impact of the Human Coronavirus (COVID-19) Lockdown on the Energy Sector: A Case Study of Sharjah, UAE," Energies, MDPI, vol. 15(4), pages 1-19, February.
    20. Aviad Navon & Ram Machlev & David Carmon & Abiodun Emmanuel Onile & Juri Belikov & Yoash Levron, 2021. "Effects of the COVID-19 Pandemic on Energy Systems and Electric Power Grids—A Review of the Challenges Ahead," Energies, MDPI, vol. 14(4), pages 1-14, February.

    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:appene:v:302:y:2021:i:c:s0306261921009181. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.