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A spatial finer electric load estimation method based on night-light satellite image

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  • Li, Peiran
  • Zhang, Haoran
  • Wang, Xin
  • Song, Xuan
  • Shibasaki, Ryosuke

Abstract

As a fundamental parameter of the electric grid, obtaining spatial electric load distribution is the premise and basis for numerous studies. As a public, world-wide, and spatialized dataset, NPP/VIIRS night-light satellite image has been long used for socio-economic information estimation, including electric consumption, while little attention has been given to the electric load estimation. Additionally, most of the previous studies were performed at a large spatial scale, which could not reflect the electric information inner a city. Therefore, this paper proposes a method to estimate electric load density at a township-level spatial scale based on NPP/VIIRS night-light satellite data. Firstly, we reveal the different fitting relationships between EC (Electric Consumption)-NLS (Night-Light Sum) and EL (Electric Load)-NLI (Night-Light Intensity). Then, we validated the spatial-scale’s influence on the estimation accuracy by experiment via generating a series of simulated datasets. After working out the super-resolution night-light image with the SRCNN (Super-Resolution Convolutional Neural Network) algorithm, we established a finer spatial estimation model. By taking a monthly data of Shanghai as a case study, we validate the model we established. The result shows that estimating electric load at township-level based on night-light satellite data is feasible, and the SRCNN algorithm can improve the performance.

Suggested Citation

  • Li, Peiran & Zhang, Haoran & Wang, Xin & Song, Xuan & Shibasaki, Ryosuke, 2020. "A spatial finer electric load estimation method based on night-light satellite image," Energy, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315838
    DOI: 10.1016/j.energy.2020.118475
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    References listed on IDEAS

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