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.
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页数:34
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共 42 条
[21]  
Indiastat, 2019, STAT CAT WIS PER CAP
[22]  
International Institute for Population Sciences (IIPS) and ICF, 2017, ORC Macro. National Family Health Survey (NFHS-3), Manipur
[23]   EFFECTS OF CLIMATE CHANGE ON ELECTRICITY CONSUMPTION: A DECOMPOSITION OF INDUSTRIAL, RESIDENTIAL, AGRICULTURAL, AND COMMERCIAL SECTORS [J].
Kim, Hyun-Gyu .
CLIMATE CHANGE ECONOMICS, 2021, 12 (04)
[24]  
Kumar S, 2018, DEMAND ANAL COOLING
[25]   Electricity demand elasticities and temperature: Evidence from panel smooth transition regression with instrumental variable approach [J].
Lee, Chien-Chiang ;
Chiu, Yi-Bin .
ENERGY ECONOMICS, 2011, 33 (05) :896-902
[26]   Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines [J].
Li, Yanting ;
He, Yong ;
Su, Yan ;
Shu, Lianjie .
APPLIED ENERGY, 2016, 180 :392-401
[27]  
Liao S. Y., 2018, MODERN EC, V9, P587, DOI [10.4236/me.2018.94038, DOI 10.4236/ME.2018.94038]
[28]   The critical role of humidity in modeling summer electricity demand across the United States [J].
Maia-Silva, Debora ;
Kumar, Rohini ;
Nateghi, Roshanak .
NATURE COMMUNICATIONS, 2020, 11 (01)
[29]  
Ministry of Statistics and Programme Implementation Government of India (MOSPI), 2021, STAT DOM PROD OTH AG
[30]   Models for mid-term electricity demand forecasting incorporating weather influences [J].
Mirasgedis, S ;
Sarafidis, Y ;
Georgopoulou, E ;
Lalas, DP ;
Moschovits, A ;
Karagiannis, F ;
Papakonstantinou, D .
ENERGY, 2006, 31 (2-3) :208-227