Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night-Time Data

被引:47
作者
Tripathy, Bismay Ranjan [1 ]
Sajjad, Haroon [2 ]
Elvidge, Christopher D. [3 ]
Ting, Yu [4 ]
Pandey, Prem Chandra [5 ,7 ]
Rani, Meenu [6 ]
Kumar, Pavan [2 ]
机构
[1] Natl Ctr Earth Sci Studies, Minist Earth Sci, Post Box 7250, Thiruvananthapuram 695011, Kerala, India
[2] Jamia Millia Islamia, Dept Geog, New Delhi 110025, India
[3] Stanford Univ, Natl Ocean & Atmospher Adm, Appl Earth Sci, Boulder, CO 80305 USA
[4] Natl Marine Data & Informat Serv, 93 Liuwei Rd, Tianjin 300171, Peoples R China
[5] Tel Aviv Univ Israel, Dept Geog, IL-6997801 Tel Aviv, Israel
[6] GB Pant Natl Inst Himalayan Environm & Sustainabl, Kosi Katarmal 263643, Almora, India
[7] Banaras Hindu Univ, Inst Environm & Sustainable Dev, Varanasi 221005, Uttar Pradesh, India
关键词
GDP; Population; Per capita electric consumption; Regression; Fisher Analysis; Remote Sensing; OPERATIONAL LINESCAN SYSTEM; SATELLITE IMAGERY; ECONOMIC-ACTIVITY; POWER CONSUMPTION; CITY LIGHTS; LAND-COVER; POPULATION; URBANIZATION; EMISSIONS; DYNAMICS;
D O I
10.1007/s00267-017-0978-1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993-2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r (2) = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management.
引用
收藏
页码:615 / 623
页数:9
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