Residential Electrical Energy                  Overview - the Brazilian
Consumption Profile in Brazil                  Eco...
Overview - the Brazilian                          Overview - the Electric Sector
Economy post -1994                       ...
Overview - the Electric Sector                       Overview - the Electric Sector
in Brazil                             ...
Overview - the Electric Sector                     Sampling Scheme used in the
in Brazil                                  ...
We construct some additional
                                                      Cluster Analysis
variables, namely :
  ...
Case Study : COELCE                            Case Study : COELCE
                                                 10 Clu...
Descriptive Statistics - whole
5 geographical zones
                                                sample
 West (26 neigh...
Average Consumption by zone                                                                                               ...
Conclusions                                       Conclusions

 In both cases, the clustering procedure           Moreover...
Upcoming SlideShare
Loading in …5
×

Residential%20 Electrical%20 Energy%20 Consumption%20 Profile%20in%20 Brazil

644 views

Published on

Residential Electricity Consumption Profiles in Brazil

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
644
On SlideShare
0
From Embeds
0
Number of Embeds
10
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Residential%20 Electrical%20 Energy%20 Consumption%20 Profile%20in%20 Brazil

  1. 1. Residential Electrical Energy Overview - the Brazilian Consumption Profile in Brazil Economy post -1994 Mônica Barros Brazilian government started in June 1994, and economic plan (named “Plano Real”) Reinaldo Castro Souza that dramatically reduced monthly inflation DEE, PUC-RIO from 80% to about 1%. August 1997 Before the advent of “Plano Real”, lower income classes had no protection against daily inflation and currency devaluations, since they had limited access to baking services and products. info@mbarros.com 1 info@mbarros.com 2 Overview - the Brazilian Overview - the Brazilian Economy post -1994 Economy post -1994 At the onset of “Plano Real”, minimum This, together with a stable currency, wage almost doubled in real terms (from caused a massive income transfer to the roughly US$ 60 to US$ 100 monthly). poorest individuals in society. The radical fall in inflation rates also Even though credit restrictions have been contributed to increase, in real terms, the imposed by Brazil’s Central Bank and disposable income of poor families, since interest rates are among the highest in the now their money has the same purchasing world, access to credit is relatively easy, power at the beginning or at the end of the especially in the electronic goods and month. automotive sectors. info@mbarros.com 3 info@mbarros.com 4
  2. 2. Overview - the Brazilian Overview - the Electric Sector Economy post -1994 in Brazil All of these factors, together with Most of the power plants are hydroelectric increasing electronics imports, caused a plants, whose construction takes a very substantial impact on electrical energy long period of time (around 10 years, in consumption, especially in the residential some cases). sector. The electric sector in Brazil has been Electricity rates (which are still under going through dramatic changes since government control) have been raised 1995. above inflation rates, but this has not State controlled companies (energy prevented consumption from experiencing producers and distributors) are being sold unprecedented growth. to private groups. info@mbarros.com 5 info@mbarros.com 6 Overview - the Electric Sector Overview - the Electric Sector in Brazil in Brazil The explosive growth in electrical energy A survey on residential electricity consumption in Brazil for the past 3 years consumption habits and holding of has made demand analysis fundamental electrical appliances was done in 1988. for planning and control. Due to technological advances and the Several efforts are currently being made to economic changes just mentioned, this create a residential consumer profile in 1988 research is obviously outdated. different areas of the country. info@mbarros.com 7 info@mbarros.com 8
  3. 3. Overview - the Electric Sector Overview - the Electric Sector in Brazil in Brazil This presentation is part of an ongoing This profile will be able to identify consulting project developed for electricity spending habits and aid in the Eletrobrás, the Brazilian Electric Sector implementation of Demand Side Holding Company. Management (DSM) policies. The objective of this project is to create a Effective implementation of DSM policies profile of residential consumers in all is crucial at this moment, since Brazil is areas of Brazil. on the verge of an electrical energy collapse, due to unexpected and unprecedented consumption growth. info@mbarros.com 9 info@mbarros.com 10 Overview - the Electric Sector Overview - the Electric Sector in Brazil in Brazil Currently, we are in the process of In 1990, residential consumption implementing surveys throughout Brazil. corresponded to 20% of total electrical energy consumed. Residential consumption is a major concern for electrical power companies in In 1996, this participation grew to 27%, Brazil, since its share in total consumption and in the years 2000-2002, it is estimated has been growing fast since 1990. at 33%. info@mbarros.com 11 info@mbarros.com 12
  4. 4. Overview - the Electric Sector Sampling Scheme used in the in Brazil survey The sample surveys currently in progress Due to the diversity in social and are important for two reasons: economic indicators throughout the Demand Side Management country, an ordinary sample plan based Identification of factors that can serve on the number of residential consumers in as explanatory variables in forecasting each town or city is not appropriate, even models for residential consumption when analyzing individual states. We propose an alternative sampling plan, where stratification is based on clustering. info@mbarros.com 13 info@mbarros.com 14 Sampling Scheme used in the Available Data for each town or survey city These clusters are created from the notion Total consumption of an “electrical distance” which Average household consumption compares consumption in each town with Total number of households whose average values for each utility company. average monthly consumption falls into each of the 10 categories: 0-30 KWh, 31-50 These clusters will serve as strata in a KWh, 51-100 KWh, 101-150 KWh, 151-200 stratified sampling procedure, in order to KWh, 201-300 KWh, 301-400 KWh, 401-500 reduce “within stratum” variance. KWh, 501-1000 KWh, above 1000 KWh. info@mbarros.com 15 info@mbarros.com 16
  5. 5. We construct some additional Cluster Analysis variables, namely : Standardized Consumption = total town Based on percentages of households in consumption standardized so that the whole each of the 10 categories. sample of towns in each state is a variable with mean zero and variance one. We start the procedure by forming n Electrical Distance = Euclidean distance clusters, where n is roughly 10 % of the computed from the percentages of households number of towns in the state. in each category for a give town ( in Algorithm used: Euclidean distances, comparison with percentages for the entire single linkage clustering. state). Percentages of households in each of the 10 consumption categories. info@mbarros.com 17 info@mbarros.com 18 Case Study: COELCE Case Study: COELCE COELCE is the energy distributor in the We start by forming 18 clusters, but State of Ceará, in the Northeastern part of several of those contained less than 3 Brazil. towns or villages. This clustering procedure is applied to all Thus, a sampling procedure based on towns in the State, except for the capital each of these clusters would not be cost- city (Fortaleza), which was subject to a efficient, which lead us to reduce the separate survey. number of clusters used. Most towns and villages in the state are This reduction is done until each cluster characterized by very small average formed contains a “reasonable” number of electricity consumption. towns. info@mbarros.com 19 info@mbarros.com 20
  6. 6. Case Study : COELCE Case Study : COELCE 10 Clusters based on percentages of In COELCE’s case we used 10 clusters, households in each category but 6 of those consisted on 3 or less Cluster num_obs average average towns, and were later condensed in 2 new 1 50 consumption 55.1 std. dev. distance std. dev. 4.2 0.22 0.03 clusters. 2 3 22 1 51 45.9 3.9 ****** 0.27 0.31 0.03 ****** 4 3 60.7 1.5 0.2 0.01 5 4 67 7 0.17 0.04 6 63 68.1 5.6 0.14 0.03 Moreover, the total number of households 7 23 87.4 6.9 0.07 0.02 8 2 72 0.21 0.14 0 in these small clusters is negligible, and 9 3 43.2 2.5 0.35 0.01 10 3 38.8 3.3 0.41 0.03 their combination doesn’t lead to ENTIRE num_obs average std. dev. average std. dev. significant losses in precision. SAMPLE consumption distance 174 63.6 13 0.18 0.08 info@mbarros.com 21 Case Study: Rio de Janeiro 5 geographical zones We conducted a preliminary study in the South (19 neighborhoods) - most affluent, city of Rio de Janeiro. but includes some shanty towns with The basic aim was to identify similar totally different consumption patterns. electricity consumption patterns among 154 neighborhoods that comprise the city. North (26 neighborhoods) - some areas Originally, the city was divided into five are upper medium class, but generally zones using a geographical criterion. lower consumption than on the south Significant differences among each of the zone. five zones are observed. info@mbarros.com 23 info@mbarros.com 24
  7. 7. Descriptive Statistics - whole 5 geographical zones sample West (26 neighborhoods) - mixed, some new residential areas but others with rural average std.dev. m um inim m um axim characteristics. consumption 192.1 54.8 97 492 p0-50 13.9 5.6 5.0 33.9 Suburban (71 neighborhoods) - low p51-100 17.6 5.3 3.4 32.0 income areas, low energy consumption. p101-150 19.2 3.9 6.9 26.7 p151-300 34.5 6.9 13.4 48.8 Center (15 neighborhoods) - around p301-500 10.6 5.3 1.9 29.2 downtown, some low income p >501 4.2 6.3 0 52.6 neighborhoods. info@mbarros.com 25 Case Study : Rio de Janeiro Average Consumption by zone ZONA: centro We consider only 6 categories of energy 300 consumo médio 250 Center consumption, namely: 200 150 0-30 KWh 100 CAJU COSME VELH CENTRO ESTACIO FLAMENGO CATETE CIDADE NOV GAMBOA GLORIA LARANJEIRA MANGUEIRA SANTA TERE SANTO CRIS SAUDE CATUMBI 31-50 KWh BAIRRO 51-100 KWh ZONA: norte 101-150 KWh 400 consumo médio 350 151-300 KWh 300 250 North 200 301-500 KWh 150 100 ALTO B VIS BANCARIOS CACUIA COCOTA FREGUESIA JD CARIOCA JD GUANABA MARACANA PAQUETA PORTUGUESA RIBEIRA S CRISTOVA TAUA TIJUCA VILA ISABE ENGENHO NO GALEAO MONERO ANDARAI C UNIVERSI PCA BANDEI RIO COMPRI ZUMBI GRAJAU PITANGUEIR PR BANDEIR above 501 KWh BAIRRO info@mbarros.com 27
  8. 8. Average Consumption by zone Average Consumption by zone ZONA: oeste 280 ZONA: sul consumo médio 260 240 West 600 consumo médio 220 550 200 180 500 450 South 160 400 140 350 120 300 250 SANTISSIMO ANIL CAMPINHO B.GUARATIB COSMOS GARD AZUL CURICICA FREGUESIA GUARATIBA INHOAIBA P.GUARATIB PACIENCIA PCA SECA PECHINCHA S VASCONCE SEPETIBA TANQUE TAQUARA CAMORIM CAMPO GRAN CIDADE DEU JACAREPAGU SANTA CRUZ 200 150 100 BAIRRO 50 0 BARRA TIJU JD BOTANIC LEBLON SAO CONRAD RC BANDEIR VARG.GRAND BOTAFOGO COPACABANA GAVEA HUMAITA IPANEMA ITANHANGA JOA LAGOA LEME ROCINHA URCA VARG.PEQUE VIDIGAL ZONA: suburbana 240 consumo médio 220 200 180 160 Suburban 140 120 BAIRRO 100 BARROS FIL VIC.CARVAL VILA PENHA ABOLICAO AGUA SANTA B RIBEIRO BONSUCESSO COLEGIO DEODORO ENG DENTRO ENG RAINHA H GURGEL INHAUMA JACARE JD AMERICA MAG BASTOS MANGUINHOS MARIOPOLIS OLARIA PAVUNA PENHA PILARES Q BOCAIUVA RAMOS LINS VASCO RIACHUELO ROCHA SAMPAIO VILA KOSMO CACHAMBI CAVALCANTI VL VALQUEI COSTA BARR TODOS SANT PADRE MIGU TURIACU BAIRRO Cluster Analysis of Cluster Analysis of neighborhoods neighborhoods We base the cluster procedure on the a ve ra g e a v e ra g e C lu s te r # o bs . a v e ra g e s td . d e v. % a b o ve % b e lo w percentages of households in each of the c o n s u m p tio n 3 0 1 KW h 1 5 1 KW h 11 1 9 7 .0 **** 2 .4 8 4 .3 6 energy consumption categories. 2 8 1 1 8 .8 6 .5 4 .0 7 5 .1 4 28 1 4 9 .3 1 1 .1 7 .3 6 4 .0 We created 12 clusters, of which 6 contain 9 5 2 1 1 7 5 .0 1 8 6 .0 1 9 .8 **** 1 5 .4 1 6 .1 6 0 .3 6 6 .9 only one neighborhood. 1 99 1 9 2 .7 2 4 .8 1 4 .1 4 7 .5 10 1 2 4 6 .0 **** 2 7 .1 4 6 .6 2 other clusters contain 2 neighborhoods 8 3 2 9 2 6 5 .0 2 9 7 .0 1 5 .6 1 9 .1 2 9 .2 3 6 .9 2 5 .1 3 1 .8 each. 7 12 1 1 3 9 1 .0 4 0 8 .0 **** **** 5 5 .6 5 6 .1 1 6 .8 2 4 .7 6 1 4 9 2 .0 **** 6 4 .9 1 9 .8 W h o le S a m p le 154 1 9 2 .1 5 4 .8 1 4 .8 5 0 .7 info@mbarros.com 31
  9. 9. Conclusions Conclusions In both cases, the clustering procedure Moreover, some of the neighborhoods results in groups that are much more singled out by the cluster procedure are homogeneous than the entire sample. clear “outliers”, that is, do not represent the entire population being sampled. In the Rio de Janeiro case study, even in cluster 1, which contains roughly 2/3 of In the Rio de Janeiro case study, clusters the sample, there is a considerable 6, 7 and 12 represent very high income reduction of variance, when compared areas of the city, as reflected by their with the whole sample of neighborhoods. energy consumption levels. info@mbarros.com 33 info@mbarros.com 34 Conclusions Also, cluster 11 indicates the neighborhood with lowest average consumption among all 154 sampled. Surprising as it might be, clusters 6 (highest average consumption) and 11 (lowest average consumption) are geographically contiguous. info@mbarros.com 35

×