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Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland

Author

Listed:
  • Tomasz Cieślik

    (Institute of Nuclear Physics PAN, Radzikowski St. 152, 31342 Kraków, Poland
    Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Mickiewicz Ave. 30, 30059 Kraków, Poland)

  • Piotr Narloch

    (Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Mickiewicz Ave. 30, 30059 Kraków, Poland
    Polish Gas Company, Bandrowskiego St. 16, 33100 Tarnów, Poland)

  • Adam Szurlej

    (Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Mickiewicz Ave. 30, 30059 Kraków, Poland)

  • Krzysztof Kogut

    (Faculty of Energy and Fuels, AGH University of Science and Technology, Mickiewicz Ave. 30, 30059 Kraków, Poland)

Abstract

In March 2020, a lockdown was imposed due to a global pandemic, which contributed to changes in the structure of the consumption of natural gas. Consumption in the industry and the power sector decreased while household consumption increased. There was also a noticeable decrease in natural gas consumption by commercial consumers. Based on collected data, such as temperature, wind strength, duration of weather events, and information about weather conditions on preceding days, models for forecasting gas consumption by commercial consumers (hotels, restaurants, and businesses) were designed, and the best model for determining the impact of the lockdown on gas consumption by the above-mentioned consumers was determined using the MAPE (mean absolute percentage error). The best model of artificial neural networks (ANN) gave a 2.17% MAPE error. The study found a significant decrease in gas consumption by commercial customers during the first lockdown period.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1393-:d:749334
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    References listed on IDEAS

    as
    1. López Prol, Javier & O, Sungmin, 2020. "Impact of COVID-19 measures on electricity consumption," MPRA Paper 101649, University Library of Munich, Germany.
    2. Siemek, Jakub & Nagy, Stanislaw & Rychlicki, Stanislaw, 2003. "Estimation of natural-gas consumption in Poland based on the logistic-curve interpretation," Applied Energy, Elsevier, vol. 75(1-2), pages 1-7, May.
    3. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
    4. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    5. Oliver, Ronan & Duffy, Aidan & Enright, Bernard & O'Connor, Rodger, 2017. "Forecasting peak-day consumption for year-ahead management of natural gas networks," Utilities Policy, Elsevier, vol. 44(C), pages 1-11.
    6. Luis M. Abadie, 2021. "Energy Market Prices in Times of COVID-19: The Case of Electricity and Natural Gas in Spain," Energies, MDPI, vol. 14(6), pages 1-17, March.
    7. Prol, Javier López & O, Sungmin, 2020. "Impact of COVID-19 Measures on Short-Term Electricity Consumption in the Most Affected EU Countries and USA States," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 23(10).
    8. 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).
    9. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    10. Sarak, H & Satman, A, 2003. "The degree-day method to estimate the residential heating natural gas consumption in Turkey: a case study," Energy, Elsevier, vol. 28(9), pages 929-939.
    11. Ruan, Guangchun & Wu, Jiahan & Zhong, Haiwang & Xia, Qing & Xie, Le, 2021. "Quantitative assessment of U.S. bulk power systems and market operations during the COVID-19 pandemic," Applied Energy, Elsevier, vol. 286(C).
    12. Yu, Yihua & Zheng, Xinye & Han, Yi, 2014. "On the demand for natural gas in urban China," Energy Policy, Elsevier, vol. 70(C), pages 57-63.
    13. 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).
    14. Xiaoyu Wang & Dongkun Luo & Jianye Liu & Wenhuan Wang & Guixin Jie, 2017. "Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO-NNGBM Model," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, November.
    15. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    16. Donia Aloui & Stéphane Goutte & Khaled Guesmi & Rafla Hchaichi, 2020. "COVID 19's impact on crude oil and natural gas S&P GS Indexes," Working Papers halshs-02613280, HAL.
    17. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    18. Hribar, Rok & Potočnik, Primož & Šilc, Jurij & Papa, Gregor, 2019. "A comparison of models for forecasting the residential natural gas demand of an urban area," Energy, Elsevier, vol. 167(C), pages 511-522.
    19. 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).
    20. Douglas B. Reynolds & Marek Kolodziej, 2009. "North American Natural Gas Supply Forecast: The Hubbert Method Including the Effects of Institutions," Energies, MDPI, vol. 2(2), pages 1-38, May.
    21. Kim, Dongwoo & Yim, Taesu & Lee, Jae Yong, 2021. "Analytical study on changes in domestic hot water use caused by COVID-19 pandemic," Energy, Elsevier, vol. 231(C).
    22. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
    23. Izadyar, Nima & Ghadamian, Hossein & Ong, Hwai Chyuan & moghadam, Zeinab & Tong, Chong Wen & Shamshirband, Shahaboddin, 2015. "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption," Energy, Elsevier, vol. 93(P2), pages 1558-1567.
    24. Gutiérrez, R. & Nafidi, A. & Gutiérrez Sánchez, R., 2005. "Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model," Applied Energy, Elsevier, vol. 80(2), pages 115-124, February.
    25. Emilio Ghiani & Marco Galici & Mario Mureddu & Fabrizio Pilo, 2020. "Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy," Energies, MDPI, vol. 13(13), pages 1-19, July.
    26. Gabriella Balacco & Vincenzo Totaro & Vito Iacobellis & Alessandro Manni & Mauro Spagnoletta & Alberto Ferruccio Piccinni, 2020. "Influence of COVID-19 Spread on Water Drinking Demand: The Case of Puglia Region (Southern Italy)," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    27. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    28. Özmen, Ayşe & Yılmaz, Yavuz & Weber, Gerhard-Wilhelm, 2018. "Natural gas consumption forecast with MARS and CMARS models for residential users," Energy Economics, Elsevier, vol. 70(C), pages 357-381.
    29. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    30. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    31. Nan Wei & Changjun Li & Jiehao Duan & Jinyuan Liu & Fanhua Zeng, 2019. "Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model," Energies, MDPI, vol. 12(2), pages 1-15, January.
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