Estimation of Regional Electricity Consumption Using National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite Night-Time Light Data with Gradient Boosting Regression Trees

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 20; p. 3841
• Main Authors: Guo, Xiaozheng, Wang, Yimei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Agglomeration; Balances (scales); China; Economic development; Economic growth; Economic indicators; Economics; Electric power; Electric power distribution; Electricity; Electricity consumption; electricity consumption estimation; Electricity distribution; Energy consumption; Energy distribution; Energy efficiency; Energy management systems; Energy use; Evaluation; GDP; gradient boosting regression trees; Gross Domestic Product; Imaging radiometers; Infrared imaging; Infrared radiometers; Light; Meteorological satellites; multiscale spatiotemporal analysis; Night; Nighttime; nighttime light remote sensing data; Power consumption; Prediction models; Radiometry; Regional development; Regression analysis; Regression models; Remote sensing; Sensors; South China Sea; South China Sea; China; Fujian China; Guangdong China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16203841

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.