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Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19

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  • Huang, Liqiao
  • Liao, Qi
  • Qiu, Rui
  • Liang, Yongtu
  • Long, Yin

Abstract

With the coronavirus pandemic wreathing havoc around the world, power industry has been hit hard due to the proposal of lockdown policies. However, the impact of lockdowns and shutdowns on the power system in different regions as well as periods of the pandemic can hardly be reflected on the foundation of current studies. In this paper, a prediction-based analysis method is developed to point out the electricity consumption gap resulted from the pandemic situation. The core of this method is a novel optimized grey prediction model, namely Rolling IMSGM(1,1) (Rolling Mechanism combined with grey model with initial condition as Maclaurin series), which achieves better prediction results in the face of long-term emergencies. A novel initial condition is adopted to track data with various characteristics in the form of higher-order polynomials, which are then determined by intelligent algorithms to realize accurate fitting. Historical power consumption data in China are utilized to carry out the monthly forecasts during COVID-19. Compared with other competitive models’ prediction results, the superiority of IMSGM(1,1) are demonstrated. Through analyzing the gap between predicted consumption values and the actual data, it can be found that the impact of the pandemic on electricity varies in different periods, which is related to its severity and the local lockdown policies. This study helps to understand the impact on power industry in the face of such an emergency intuitively so as to respond to possible future events.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920317219
    DOI: 10.1016/j.apenergy.2020.116339
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    5. 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.
    6. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    7. Hu, Chenxi & Zhang, Jun & Yuan, Hongxia & Gao, Tianlu & Jiang, Huaiguang & Yan, Jing & Wenzhong Gao, David & Wang, Fei-Yue, 2022. "Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19," Applied Energy, Elsevier, vol. 309(C).
    8. Muhammad Shahid Mastoi & Hafiz Mudassir Munir & Shenxian Zhuang & Mannan Hassan & Muhammad Usman & Ahmad Alahmadi & Basem Alamri, 2022. "A Critical Analysis of the Impact of Pandemic on China’s Electricity Usage Patterns and the Global Development of Renewable Energy," IJERPH, MDPI, vol. 19(8), pages 1-30, April.
    9. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).

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