A spatial finer electric load estimation method based on night-light satellite image. (15th October 2020)

• Record Type: Journal Article
• Authors: Li, Peiran; Zhang, Haoran; Wang, Xin; Song, Xuan; Shibasaki, Ryosuke
• Is Part Of: Energy. Volume 209(2020)
• Publication Date: 2020-10-15
• Subjects: Night-light image -- Electric load -- Spatial scale -- Super-resolution -- Deep learning; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118475
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: Electric load estimation is conducted based on night-light satellite data. Fitting relationships between electric Load and night-light intensity is revealed. The spatial-scale's influence on the estimation model fitting is validated. A finer electric load estimation method based on the SRCNN algorithm is conducted.