Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
Source abstract
Abstract
The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia’s greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Impact of Light on Environment and Health · Energy and Environment Impacts · Urban Transport and Accessibility
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10094, China
University of Chinese Academy of Sciences, Beijing 10049, China
School of Economics and Management, Southwest Forestry University, Kunming 650224, China
School of Civil Engineering, Southwest Forestry University, Kunming 650224, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10094, China
International Research Centre of Big Data for Sustainable Development Goals, Beijing 10094, China
Funding
Strategic Priority Research Program of the Chinese Academy of Science
References (44)
Show source-provided references
1. Rajkumari, L. (2020). Relation between electricity consumption and economic growth in Karnataka, India: An aggregate and sector-wise analysis. Electr. J., 33.10.1016/j.tej.2020.106768 ↗
2. Does (better) electricity supply increase household enterprise income in India?10.1016/j.enpol.2013.02.025 ↗
3. Nighttime images of the earth from space10.1038/scientificamerican0778-86 ↗
4. Remote sensing of night lights: A review and an outlook for the future10.1016/j.rse.2019.111443 ↗
5. A self-adaptive approach for producing clear-sky composites from VIIRS surface reflectance datasets10.1016/j.isprsjprs.2018.07.009 ↗
6. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration10.5194/essd-13-889-2021 ↗
7. A harmonized global nighttime light dataset 1992–201810.1038/s41597-020-0510-y ↗
8. Tian, H.F., Wang, Y.J., Chen, T., Zhang, L.J., and Qin, Y.C. (2021). Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery. Remote Sens., 13.10.3390/rs13193822 ↗
9. Summer Maize Mapping by Compositing Time Series Sentinel-1A Imagery Based on Crop Growth Cycles10.1007/s12524-021-01428-0 ↗
10. Tian, H., Chen, T., Li, Q., Mei, Q., Wang, S., Yang, M., Wang, Y., and Qin, Y. (2022). A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery. Remote Sens., 14.10.3390/rs14051113 ↗
11. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption10.1080/014311697218485 ↗
12. Modeling the population of China using DMSP operational linescan system nighttime data
13. Combining satellite imagery and machine learning to predict poverty10.1126/science.aaf7894 ↗
14. Creating a Global Grid of Distributed Fossil Fuel CO2 Emissions from Nighttime Satellite Imagery10.3390/en3121895 ↗
15. Ou, J.P., Liu, X.P., Li, X., Li, M.F., and Li, W.K. (2015). Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data. PLoS ONE, 10.10.1371/journal.pone.0138310 ↗
16. A global record of annual urban dynamics (1992–2013) from nighttime lights10.1016/j.rse.2018.10.015 ↗
17. Understanding an urbanizing planet: Strategic directions for remote sensing10.1016/j.rse.2019.04.020 ↗
18. A cluster-based method to map urban area from DMSP/OLS nightlights10.1016/j.rse.2014.03.004 ↗
19. Zhao, X.Z., Yu, B.L., Liu, Y., Yao, S.J., Lian, T., Chen, L.J., Yang, C.S., Chen, Z.Q., and Wu, J.P. (2018). NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies. Remote Sens., 10.10.3390/rs10101526 ↗
20. Li, X., Liu, S.S., Jendryke, M., Li, D.R., and Wu, C.Q. (2018). Night-Time Light Dynamics during the Iraqi Civil War. Remote Sens., 10.10.3390/rs10060858 ↗
21. Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War10.1080/01431161.2017.1331476 ↗
22. Emerging threat of the 21st century lightscape to global biodiversity10.1111/gcb.14146 ↗
23. Active forest fire monitoring in ttaranchal state, India using multi-temporal DMSP-OLS and MODIS data10.1080/01431160600810609 ↗
24. Rating the Effectiveness of Fishery Closures with Visible Infrared Imaging Radiometer Suite Boat Detection Data10.3389/fmars.2018.00132 ↗
25. Cole, T.A., Wanik, D.W., Molthan, A.L., Roman, M.O., and Griffin, R.E. (2017). Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas. Remote Sens., 9.10.3390/rs9030286 ↗
26. Shah, Z., and Taneja, J. (2019, January 13–14). Poster Abstract: Monitoring Electric Grid Reliability Using Satellite Data. Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys), New York, NY, USA.10.1145/3360322.3361010 ↗
27. A high-resolution gridded dataset to assess electrification in sub-Saharan Africa10.1038/s41597-019-0122-6 ↗
28. Multiscale assessment of progress of electrification in Indonesia based on brightness level derived from nighttime satellite imagery10.1007/s10661-017-5949-8 ↗
29. Principe, J., and Takeuchi, W. (2019). Supply and Demand Assessment of Solar PV as Off-Grid Option in Asia Pacific Region with Remotely Sensed Data. Remote Sens., 11.10.3390/rs11192255 ↗
30. Letu, H., Hara, M., Yagi, H., Tana, G., and Nishio, F. (2009, January 20–22). Estimating the energy consumption with nighttime city light from the DMSP/OLS imagery. Proceedings of the Joint Workshop on Urban Remote Sensing, Shanghai, China.10.1109/URS.2009.5137699 ↗
31. Spatial characterization of electrical power consumption patterns over India using temporal DMSP-OLS night-time satellite data10.1080/01431160802345685 ↗
32. Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data10.3390/rs6021705 ↗
33. Comparison of Radiation Intensity and Estimation of Electric Power Consumption between DMSP/OLS and VIIRS Nighttime Light Images
34. Modelling regional socio-economic parameters based on comparison of NPP/VIIRS and DMSP/OLS nighttime light imagery
35. An approach of GDP spatialization in Hebei province using NPP-VIIRS nighttime light data
36. Method for SNPP-VIIRS nighttime lights images denoising
37. Spatial distribution of population data based on nighttime light and LUC data in the Sichuan-Chongqing Region
38. Correction of DMSP/OLS night-time light images and its application in China
39. Regional hydrologic analysis 1. ordinary, weighted, and generalized least-squares compared10.1029/WR021i009p01421 ↗
40. Rivera-Gonzalez, L., Bolonio, D., Mazadiego, L.F., and Valencia-Chapi, R. (2019). Long-Term Electricity Supply and Demand Forecast (2018–2040): A LEAP Model Application towards a Sustainable Power Generation System in Ecuador. Sustainability, 11.10.3390/su11195316 ↗
41. The long-term forecast of Pakistan’s electricity supply and demand: An application of long range energy alternatives planning10.1016/j.energy.2015.10.103 ↗
42. Shah, I., and Lisi, F. (2015, January 19–22). Day-ahead electricity demand forecasting with nonparametric functional models. Proceedings of the 12th International Conference on the European Energy Market (EEM), Lisbon, Portugal.10.1109/EEM.2015.7216741 ↗
43. Shah, I., Iftikhar, H., Ali, S., and Wang, D.P. (2019). Short-Term Electricity Demand Forecasting Using Components Estimation Technique. Energies, 12.10.3390/en12132532 ↗
44. Energy time series forecasting-analytical and empirical assessment of conventional and machine learning models10.3233/JIFS-201717 ↗