Environmental Management DOI 10.1007/s00267-017-0978-1 Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night- Time Data Bismay Ranjan Tripathy 1 ● Haroon Sajjad 2 ● Christopher D. Elvidge 3 ● Yu Ting 4 ● Prem Chandra Pandey 5,7 ● Meenu Rani 6 ● Pavan Kumar 2 Received: 13 May 2017 / Accepted: 6 December 2017 © Springer Science+Business Media, LLC, part of Springer Nature 2017 Abstract Changes in the pattern of electric power con- sumption in India have influenced energy utilization pro- cesses and socio-economic development to greater extent during the last few decades. Assessment of spatial dis- tribution 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, elec- tricity 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. Keywords GDP ● Population ● Per capita electric consumption ● Regression ● Fisher Analysis ● Remote Sensing Introduction Defense meteorological satellite program-operational line- scan system (DMSP-OLS) has unique ability to map the night-time light data due to its photo-electric zooming capacity. DMSP-OLS night-time data (NOAA/NGDC) has great prospects to analyze urban expansion (Elvidge et al. 2007; McDonald et al. 2008), mapping of urban extent (Li and Zhou 2017), population (size, density, and growth) (Amaral et al. 2006; Lo 2001; Sun et al. 2017), energy consumption (Chand et al. 2009; Elvidge et al. 2011), economic growth (Doll et al. 2006; Elvidge et al. 2009; Sutton et al. 2007) and disaster events (Elvidge et al. 2001). The analysis of these parameters has been made possible due to capacity of the DMPS-OLS to capture radiation primarily from human sourced city lights such as lanterns, flares, industrial lights and natural lights such as lightening and fires (Chand et al. 2009). The civilian remote sensing application of DMSP-OLS night-time data has been * Pavan Kumar pavan.jamia@gmail.com 1 National Centre for Earth Science Studies (Ministry of Earth Sciences), Post Box No.7250, Akkulam, Thiruvananthapuram 695011, India 2 Department of Geography, Jamia Millia Islamia, New Delhi 110025, India 3 Applied Earth Sciences, National Oceanic and Atmospheric Administration, Stanford University, Boulder, CO 80305, USA 4 National Marine Data and Information Service, No. 93 Liuwei Road, Hedong District, Tianjin 300171, China 5 Department of Geography, Tel Aviv University Israel, Tel Aviv 6997801, Israel 6 G.B. Pant National Institute of Himalayan Environment & Sustainable Development, Kosi-Katarmal, Almora 263643, India 7 Present address: Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India