Paper Info
Title
An Assessment of Electric Power Consumption Using Random Forest and Transferable Deep Model with Multi-Source Data
Abstract
Reliable and fine-resolution electric power consumption (EPC) is essential for effective urban electricity allocation and planning. Currently, EPC data exists mainly as statistics with low resolution. Many studies estimate fine-resolution EPC based on the positive correction between stable nighttime light and EPC distribution. However, EPC is related to various factors other than nighttime light and is spatially non-stationary. Yet this has been ignored in current research. This study developed a novel method to estimate EPC at 500 m resolution by considering spatially non-stationary through fusing geospatial data and high-resolution satellite images. Deep transfer learning and statistical methods were used to extract socio-economic, population density, and landscape features to describe EPC distribution from multi-source geospatial data. Finally, a random forest regression (RFR) model with features and EPC statistics is established to estimate fine-resolution EPC. A study area of Shenzhen city, China, is employed to evaluate the proposed method. The R-2 between predicted EPC and statistical EPC is 0.82 at sub-district level in 2013, which is higher than an existing EPC product (Shi's product) with R-2 = 0.46, illustrating the effectiveness of the proposed method. Moreover, the EPC distribution for Shenzhen from 2013 to 2019 was estimated. Furthermore, the spatiotemporal dynamic of EPC was analyzed at the pixel and sub-district levels.
Year
DOI
Venue
2022
10.3390/rs14061469
REMOTE SENSING
Keywords
DocType
Volume
remote sensing, electric power consumption, high-resolution satellite imagery, nighttime light imagery, random forest, transferable deep model
Journal
14
Issue
ISSN
Citations 
6
2072-4292
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Luxiao Cheng101.01
Ruyi Feng213.41
Lizhe Wang32973191.46
Jining Yan433.78
Liang Dong532652.32