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Author Falchetta, G.; Noussan, M.
Title Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region Type Journal Article
Year 2019 Publication (up) Energies Abbreviated Journal Energies
Volume 12 Issue 3 Pages 456
Keywords Remote Sensing; Energy
Abstract Using remotely-sensed Suomi National Polar-orbiting Partnership (NPP)-VIIRS (Visible Infrared Imagery Radiometer Suite) night-time light (NTL) imagery between 2012 and 2016 and electricity consumption data from the IEA World Energy Balance database, we assemble a five-year panel dataset to evaluate if and to what extent NTL data are able to capture interannual changes in electricity consumption within different countries worldwide. We analyze the strength of the relationship both across World Bank income categories and between regional clusters, and we evaluate the heterogeneity of the link for different sectors of consumption. Our results show that interannual variation in nighttime light radiance is an effective proxy for predicting within-country changes in power consumption across all sectors, but only in lower-middle income countries. The result is robust to different econometric specifications. We discuss the key reasons behind this finding. The regions of Sub-Saharan Africa, Middle-East and North Africa, Latin America and the Caribbeans, and East Asia and the Pacific render a significant outcome, while changes in Europe, North America and South Asia are not successfully predicted by NTL. The designed methodological steps to process the raw data and the findings of the analysis improve the design and application of predictive models for electricity consumption based on NTL at different spatio-temporal scales.
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Series Volume Series Issue Edition
ISSN 1996-1073 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2200
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Author Jasiński, T.
Title Modeling electricity consumption using nighttime light images and artificial neural networks Type Journal Article
Year 2019 Publication (up) Energy Abbreviated Journal Energy
Volume 179 Issue Pages 831-842
Keywords Remote Sensing
Abstract The purpose of this paper is to model electricity consumption using Artificial Neural Networks (ANN). Total electricity consumption and consumption generated by households (HH) were modeled. The input variables of the ANN were based on nighttime light images from VIIRS DNB. Studies conducted thus far have covered mainly linear models. Most of case studies focused on single countries or groups of countries with only few focusing on the sub-national scale. This paper is pioneering in covering an area of Poland (Central Europe) at NUTS-2 level. The use of ANN enabled the modeling of the non-linear relations associated with the complex structure of electricity demand. Satellite data were collected for the period 2013–2016, and included images with improved quality (inter alia higher resolution), compared to the DMSP/OLS program. As images are available from April 2012 onwards, it is only recently that their number has become sufficient for ANN learning. The images were used to create models of multilayer perceptrons. The results achieved by ANN were compared with the results obtained using linear regressions. Studies have confirmed that electricity consumption can be determined with higher precision by the ANN method.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0360-5442 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2475
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Author Shi, K.; Yu, B.; Huang, C.; Wu, J.; Sun, X.
Title Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road Type Journal Article
Year 2018 Publication (up) Energy Abbreviated Journal Energy
Volume 150 Issue Pages 847-859
Keywords Remote Sensing
Abstract Fully understanding spatiotemporal patterns of electric power consumption (EPC) is one of the key questions related to sustainable socioeconomic and environmental development in countries along the Silk Road Economic Belt and the 21st-Century Maritime Silk Road (hereinafter referred to as the Belt and Road countries). However, studies about spatiotemporal patterns of EPC in the Belt and Road countries are still scarce due to the lack of reliable data. This study attempted to investigate spatiotemporal patterns of EPC in the Belt and Road countries from multiple perspectives. Firstly, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime stable light data were used to estimate EPC from 1992 to 2013. Subsequently, the mathematical statistic method, standard deviational ellipse, rank size rule, and correlation analysis were employed to evaluate the EPC change in detail. The results reveal that the EPC growth mainly occurs in the developing countries, especially in China. The geographical distribution of EPC in the Belt and Road countries is oriented in the Northwest-Southeast direction between 1992 and 2013. Based on the rank size rule analysis, the slope values of q are −2.392 and −2.175 between 1992 and 2013, with an average R2 value of 0.664, indicating a clear clustering pattern of EPC. It is also proved that GDP is a more important impact factor to EPC than the population. Our findings can offer an effective way to understand spatiotemporal evolution characteristics of EPC in the Belt and Road countries, and provide references for regional socioeconomic development and cooperation.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0360-5442 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2487
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Author Xie, Y.; Weng, Q.
Title Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries Type Journal Article
Year 2016 Publication (up) Energy Abbreviated Journal Energy
Volume 100 Issue Pages 177-189
Keywords Remote Sensing
Abstract A better understanding of the spatiotemporal pattern of energy consumption at the urban scale is significant in the interactions between economic activities and environment. This study assessed the spatiotemporal dynamics of EC (electricity consumption) in UC (urban cores) and SR (suburban regions) in China from 2000 to 2012 by using remotely sensed NTL (nighttime light) imagery. Firstly, UC and SR were extracted using a threshold technique. Next, provincial level model was calibrated yearly by using Enhanced Vegetation Index and population-adjusted NTL data as independent variables. These models were then applied for pixel-based estimation to obtain time-series EC data sets. Finally, the spatiotemporal pattern of EC in both UC and SR were explored. The results indicated that the proportion of EC in urban areas rose from 50.6% to 71.32%, with a growing trend of spatial autocorrelation. Cities with high urban EC were either located in the coastal region or belonged to provincial capitals. These cities experienced a moderate to a rapid growth of EC in both UC and SR, while a slow growth was detected for the majority of western and northeastern cities. The findings suggested that EC in SR was more crucial for sustainable energy development in China.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0360-5442 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2489
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Author Li, S.; Cheng, L.; Liu, X.; Mao, J.; Wu, J.; Li, M.
Title City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data Type Journal Article
Year 2019 Publication (up) Energy Abbreviated Journal Energy
Volume 189 Issue Pages 116040
Keywords Energy; Remote Sensing; China; electric power consumption; Night lights; Nighttime light; VIIRS-DNB
Abstract Accelerating urbanization has created tremendous pressure on the global environment and energy supply, making accurate estimates of energy use of great importance. Most current models for estimating electric power consumption (EPC) from nighttime light (NTL) imagery are oversimplified, ignoring influential social and economic factors. Here we propose first classifying cities by economic focus and then separately estimating each category’s EPC using NTL data. We tested this approach using statistical employment data for 198 Chinese cities, 2015 NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS), and annual electricity consumption statistics. We used cluster analysis of employment by sector to divide the cities into three types (industrial, service, and technology and education), then established a linear regression model for each city's NTL and EPC. Compared with the estimation results before city classification (R2: 0.785), the R2 of the separately modeled service cities and technology and education cities increased to 0.866 and 0.830, respectively. However, the results for industrial cities were less consistent due to their more complex energy consumption structure. In general, using classification before modeling helps reflect factors affecting the relationship between EPC and NTL, making the estimation process more reasonable and improving the accuracy of the results.
Address School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0360-5442 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2672
Permanent link to this record