Tripathy Bismay Ranjan, Sajjad Haroon, Elvidge Christopher D, Ting Yu, Pandey Prem Chandra, Rani Meenu, Kumar Pavan
National Centre for Earth Science Studies (Ministry of Earth Sciences), Post Box No.7250, Akkulam, Thiruvananthapuram, 695011, India.
Department of Geography, Jamia Millia Islamia, New Delhi, 110025, India.
Environ Manage. 2018 Apr;61(4):615-623. doi: 10.1007/s00267-017-0978-1. Epub 2017 Dec 27.
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 = 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.
在过去几十年里，印度电力消费模式的变化在很大程度上影响了能源利用过程和社会经济发展。因此，评估电力消费的空间分布对于预测能源资源的可用性及其基础设施规划至关重要。本文试图对印度可持续能源的未来电力需求及其管理进行建模。夜间灯光数据库能很好地近似能源的可用性。我们利用国防气象卫星计划业务线扫描系统（DMSP - OLS）夜间卫星数据、电力消费（1993 - 2013年）、国内生产总值（GDP）和人口增长来构建模型。我们还试图检验电力消费对GDP和人口增长的敏感性。结果表明，校准后的DMSP和模型提供了关于电力需求与GDP和人口相关的现实信息，相关系数r = 0.91，准确性更高。通过相关性分析发现，电力需求对GDP（r = 0.96）的敏感性高于对人口增长（r = 0.76）的敏感性。因此，该模型被证明是预测电力需求以实现其可持续利用和管理的有用工具。
