Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil

被引：11
|
作者：
Cabral, Joilson de Assis [1 ,2 ]
Cabral, Maria Viviana de Freitas [1 ,3 ]
Pereira Junior, Amaro Olimpio [4 ]
机构：
[1] Univ Fed Rural Rio de Janeiro, Dept Econ, Reg Econ & Dev Programme, Inst Appl Social Sci, BR 465,KM 7, BR-23897000 Seropedica, RJ, Brazil
[2] Univ Fed Rural Rio de Janeiro, Dept Econ, Management & Strategy Programme, Inst Appl Social Sci, BR 465,KM 7, BR-23897000 Seropedica, RJ, Brazil
[3] Univ Fed Rural Rio de Janeiro, Dept Econ, Terr Dev & Publ Policy Programme, Inst Appl Social Sci, BR 465,KM 7, BR-23897000 Seropedica, RJ, Brazil
[4] Univ Fed Rio de Janeiro, Energy Planning Programme, Ctr Tecnol, Ilha Fundao, Bloco C,Sala 211,Cidade Univ, BR-21941972 Rio De Janeiro, RJ, Brazil
来源：
UTILITIES POLICY | 2020年 / 66卷
关键词：
Spatiotemporal models; Brazilian residential electricity demand; Elasticities and forecasting performance; PRICE ELASTICITY; NEURAL-NETWORKS; PANEL-DATA; CONSUMPTION; INCOME; MODEL; SPECIFICATION; CONVERGENCE; SPACE; TESTS;
D O I：
10.1016/j.jup.2020.101108
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Spatiotemporal models to estimate electricity demand are scarce in the existing literature. In this paper, we compare three models to estimate elasticities and forecast demand for residential electricity in Brazil. The Dynamic Spatial Durbin Model presented the best goodness of fit, with results that confirm the need to consider spatial dependence in the Brazilian regions. The results showed temporal inertia, inelasticity of demand concerning price and income, and a significant impact of the temperature and the number of households connected to the grid. We conclude that omitting the spatiotemporal dynamic could lead to bias in the models used by Brazilian utilities.
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收藏
页数：8
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