Journal of Applied Sciences ›› 2021, Vol. 39 ›› Issue (3): 508-520.doi: 10.3969/j.issn.0255-8297.2021.03.015

• Computer Science and Applications • Previous Articles    

Multiscale Analysis on Spatiotemporal Pattern of Electric Power Consumption in China——Based on DMSP-OLS Nighttime Light Data

WANG Tao1,2, FENG Zhichang1, LUO Jian1, MENG Qingtao1   

  1. 1. C-EPRI Electrical Engineering Co., Ltd, Beijing 102200, China;
    2. Electrical and Mechanical Branch, Beijing Industry and Trade Technicians College, Beijing 100097, China
  • Received:2019-10-16 Published:2021-06-08

Abstract: In combination of the corrected operational linescan system on defense meteorological satellite program (DMSP-OLS) nighttime light data with provincial electric power consumptions, a provincial scale electric power consumption simulation model was structured for the years from 2000 to 2013. The fitting goodness of the model was tested to be 0.714 9, and this proved the effectiveness of the proposed model. On this basis, the electric power consumptions of prefectural, county and pixel scales were calculated, and the spatiotemporal pattern of multiscale electric power consumption was analyzed by global spatial autocorrelation and local spatial autocorrelation. Computation results indicated that the electric power consumption in China showed a significant growth trend from 2000 to 2013. Total electric power consumption increased from 1 360.629 billion kWh in 2000 to 5 342.339 billion kWh in 2013. It is also found that the Beijing-Tianjin-Hebei region, the Yangtze River Delta and the Pearl River Delta were the high electric power consumption zones, while the western region presented low electric power consumption. This power consumption distribution showed the characteristics of Hu line. The power consumptions of all different scales presented a consistent spatial distribution trend, and there were differences of electric power consumption in different regions. Prefectural scale was an effective administrative unit to simulate electric power consumption. 12.9% of provinces, 16.56% of prefectural, and 40.95% of counties in 2013 showed local spatial correlation in electric power consumption.

Key words: multiscale, electric power consumption, nighttime light data, global spatial autocorrelation, local spatial autocorrelation

CLC Number: