DATA ANALISYS VIA GLIM: MODELLING THE RESIDENCIAL ELECTRICITY CONSUMPTION OF JUIZ DE FOR A, MINAS GERAIS / SOBRE A ANÁLISE DE DADOS GLIM: MODELAGEM DO CONSUMO RESIDENCIAL DE ENERGIA ELÉTRICA EM JUIZ DE FORA, MINAS GERAIS

AUTOR(ES)
DATA DE PUBLICAÇÃO

1996

RESUMO

The purpose of this study is to estimate model of residential consumption of electrical energy in Juiz de Fora, MG, using the computer program GLIM and to compare such results with the ones obtained when a classical regression model are employed. A data representanting the findings of 593 dweling in the city of Juiz de Fora was used in this study. Some data weren´t yet survey considered outliers and could conduce false results and consequenly sing as they were bias the conclusions. This dissertation has an introduction about the problems related to the consumption of electrical energy in Brazil and the world an the solutions proposed to solve it through demand side management (DSM). The consumption was assumed to have a gamma distribution. The data set was divided into four ranges of consumption and one model for each range was estimated. A generalized linear model in this task which can be consideraded a classical regresssion model when consumption is supposed to have a normal distribution. The results from the error function and the generalized Pearson´statistic pointed towards the use of a Gamma probability function for consumption due to a slight positive skeweness shown by the data. The data matrix presented some null columns values for a number of appliances; the repetition of the process using more dense matrix is recommended. The differences found for a normal function and a Gamma distributions were significant only for the values of the errors function and the generalized Pearson statistic. The coefficients of explanation in both cases were under similar conditions, perhaps due to the very slight positive skewness of this response variable. The dissertation is conclude by recommending the use of Generalized Linear Models for the greater flexibility when compared with classic models; besides, the GLIM software is recommended for this estimation of the model parameters.

ASSUNTO(S)

electrical energy consumption consumo de energia eletrica data analysis analise de dados

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