Implications of the COVID-19 Pandemic on China's Power Consumption: A comprehensive Analysis Derived from Monthly Electricity Consumption Forecasts

Fengting Wu

doi:10.54097/jid.v4i1.10760

Authors

Fengting Wu

DOI:

https://doi.org/10.54097/jid.v4i1.10760

Keywords:

COVID-19, Electricity Consumption, FDDGSM Model

Abstract

The COVID-19 pandemic profoundly altered the total and pattern of electricity consumption across Chinese industries and households. However, current research has not fully and accurately measured the impact of the pandemic. This paper builds a novel discrete gray seasonal model (referred to as the FDDGSM model) to predict the scenario of different domestic electricity-consuming sectors and residential electricity consumption without a pandemic backdrop, by comparing it with the actual electricity consumption in 2020, the impact of the COVID-19 pandemic on China's electricity consumption is better revealed. Through empirical analysis, the following conclusions are reached in this study: (1) The novel discrete grey season model shows more stable and accurate characteristics in forecasting time series with seasonal trends. (2) The pandemic did not significantly impact electricity consumption in primary industries and households, while actual usage in secondary and tertiary industries was significantly lower than expected in a non-pandemic scenario. (3) The impact on secondary and tertiary industries varied in magnitude and duration, and they differed in the rate of recovery in electricity consumption.

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References

NATIONAL BUREAU OF STATISTICS. China Statistical Yearbook [M]. 2021.

KAMYAB H, NADERIPOUR A, JAHANNOUSH M, et al. Potential effect of SARS-CoV-2 on solar energy generation: Environmental dynamics and implications[J]. Sustainable Energy Technologies and Assessments, 2022, 52: 102027.

KHURSHID A, KHAN K. How COVID-19 shock will drive the economy and climate? A data-driven approach to model and forecast[J/OL]. Environmental Science and Pollution Research, 2021, 28(3): 2948-2958.

JULONG D. Introduction to grey system theory[J]. The Journal of grey system, 1989, 1(1): 1-24.

CHANG C J, CHEN C C, DAI W L, et al. A new grey prediction model considering the data gap compensation[J]. Grey Systems: Theory and Application, 2021, 11(4): 650-663.

LIU C, LAO T, WU W Z, et al. Application of optimized fractional grey model-based variable background value to predict electricity consumption[J]. Fractals, 2021, 29(02): 2150038.

XIE N ming, LIU S feng. Discrete grey forecasting model and its optimization[J]. Applied mathematical modelling, 2009, 33 (2): 1173-1186.

ZENG B, DUAN H, ZHOU Y. A new multivariable grey prediction model with structure compatibility[J]. Applied Mathematical Modelling, 2019, 75: 385-397.

KAYACAN E, ULUTAS B, KAYNAK O. Grey system theory-based models in time series prediction[J]. Expert systems with applications, 2010, 37(2): 1784-1789.

LEE P C, YE B, LO T P, et al. A dynamic weighting adjustment algorithm for hybrid gray model based on artificial neural network[J]. Journal of the Chinese Institute of Engineers, 2020, 43(3): 249-256.

National Bureau of Statistics. CHINA ENERGY STATISTICAL YEARBOOK[M]. 2021.

WANG Z X, LI Q, PEI L L. A seasonal GM (1, 1) model for forecasting the electricity consumption of the primary economic sectors[J]. Energy, 2018, 154: 522-534.

BENVENUTO D, GIOVANETTI M, VASSALLO L, et al. Application of the ARIMA model on the COVID-2019 epidemic dataset[J]. Data in brief, 2020, 29: 105340.

WANG S, WEI F, LI H, et al. Comparison of SARIMA model and Holt-Winters model in predicting the incidence of Sjögren’s syndrome[J]. International Journal of Rheumatic Diseases, 2022, 25(11): 1263-1269.