CLIMATE SENSITIVITY OF ELECTRICITY CONSUMPTION AND PEAK DEMAND IN INDIA: CASE OF HETEROGENEOUS CLIMATE ZONES

被引:2
作者
Jain, Divya [1 ]
Sarangi, Gopal K. [1 ]
Das, Sukanya [1 ]
机构
[1] TERI Sch Adv Studies, 10 Inst Area, New Delhi 110070, India
关键词
Climate variability; electricity demand; multivariate adaptive regression splines; India; ADAPTIVE REGRESSION SPLINES; TEMPERATURE; LOAD; DELHI; MODEL; CITY;
D O I
10.1142/S2010007823500136
中图分类号
F [经济];
学科分类号
02 ;
摘要
Electricity demand is determined largely by regional climate conditions and seasonal characteristics, apart from a myriad of socio-economic and demographic factors. This paper investigates the climate sensitivity of electricity consumption and peak demand in six energy-intensive Indian states across heterogeneous climate zones using a non-parametric approach known as multivariate adaptive regression splines. The results show the highest temperature sensitivity of cooling electricity consumption in Punjab (8.2%), followed by Rajasthan (3.5%), Madhya Pradesh (3.1%), Tamil Nadu (2.3%), and Uttar Pradesh (1.2%). Among other climate variables, relative humidity has a non-linear impact on electricity consumption in the majority of states. The minimum temperature rise has a stronger influence on peak electricity demand than the maximum temperature in three states. Given that air-conditioning penetration is expected to increase in the future, this state-level analysis will help in developing accurate forecasts for electricity demand and formulating climate adaptation strategies for India.
引用
收藏
页数:34
相关论文
共 42 条
[1]   Climate change impacts on electricity demand in the State of New South Wales, Australia [J].
Ahmed, T. ;
Muttaqi, K. M. ;
Agalgaonkar, A. P. .
APPLIED ENERGY, 2012, 98 :376-383
[2]   Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region [J].
Alipour, Panteha ;
Mukherjee, Sayanti ;
Nateghi, Roshanak .
ENERGY, 2019, 185 :1143-1153
[3]   Climatic influence on electricity consumption: The case of Singapore and Hong Kong [J].
Ang, B. W. ;
Wang, H. ;
Ma, Xiaojing .
ENERGY, 2017, 127 :534-543
[4]   Relationships between meteorological variables and monthly electricity demand [J].
Apadula, Francesco ;
Bassini, Alessandra ;
Elli, Alberto ;
Scapin, Simone .
APPLIED ENERGY, 2012, 98 :346-356
[5]  
Bessec M, 2007, JEL CLASSIFICATION C, V33, pQ41
[6]  
Bureau Of Energy Efficiency Ministry of Power Government of India, 2017, ENERGY CONSERVATION
[7]  
CEA, 2019, LOAD GEN BAL REP 201
[8]  
Central Electricity Authority (CEA), 2021, LOAD GEN BAL REP 202
[9]   A new approach to modeling the effects of temperature fluctuations on monthly electricity demand [J].
Chang, Yoosoon ;
Kim, Chang Sik ;
Miller, J. Isaac ;
Park, Joon Y. ;
Park, Sungkeun .
ENERGY ECONOMICS, 2016, 60 :206-216
[10]   Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines [J].
Chou, SM ;
Lee, TS ;
Shao, YE ;
Chen, IF .
EXPERT SYSTEMS WITH APPLICATIONS, 2004, 27 (01) :133-142