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Prediction of industrial power consumption in Jiangsu Province by regression model of time variable

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  • Ma, Haoran

Abstract

Industry has always been an important driving force to promote social and economic development, and the development of industry is inseparable from energy consumption. In the process of modern production, more and more modern advanced equipment is put into use, and the main power source of these equipment is electricity. However, the production of electricity is limited by conditions. Therefore, the main purpose of this paper is to simulate and forecast the industrial power consumption of Jiangsu Province through the nonlinear transformation of time variables, so that the industrial enterprises in Jiangsu can reasonably arrange the next power demand and ensure the smooth progress of industrial activities. The final research results show that the time series regression prediction model proposed in this paper can effectively simulate and predict the results of industrial power consumption, with an accuracy of 1.02 %.

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  • Ma, Haoran, 2022. "Prediction of industrial power consumption in Jiangsu Province by regression model of time variable," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023410
    DOI: 10.1016/j.energy.2021.122093
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Chonghui & Bai, Chen & Su, Weihua & Balezentis, Tomas, 2024. "The centralised data envelopment analysis model integrated with cost information and utility theory for power price setting under carbon peak strategy at the firm-level," Energy, Elsevier, vol. 292(C).
    2. Feng Dong & Guoqing Li & Yajie Liu & Qing Xu & Caixia Li, 2023. "Spatial-Temporal Evolution and Cross-Industry Synergy of Carbon Emissions: Evidence from Key Industries in the City in Jiangsu Province, China," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    3. Witanowski, Łukasz & Klonowicz, Piotr & Lampart, Piotr & Klimaszewski, Piotr & Suchocki, Tomasz & Jędrzejewski, Łukasz & Zaniewski, Dawid & Ziółkowski, Paweł, 2023. "Impact of rotor geometry optimization on the off-design ORC turbine performance," Energy, Elsevier, vol. 265(C).
    4. 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).
    5. Dong, Jia & Li, Cunbin, 2022. "Scenario prediction and decoupling analysis of carbon emission in Jiangsu Province, China," Technological Forecasting and Social Change, Elsevier, vol. 185(C).

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