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Forecasting residential electricity consumption: a bottom-up approach for Brazil by region

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Abstract

In this paper, we forecast residential electricity consumption in Brazil considering the variability in consumption behavior in different regions. In order to do so, we use a bottom-up approach to estimate long-term electricity consumption that considers three technology-driven scenarios: one assuming reference efficiency development, another one based on the Brazilian government’s predictions, and the third one assuming high efficiency. The results show an increase of residential electricity consumption due to the increasing appliance ownership rate, especially air conditioning, as well as the increasing number of households. In addition, it is shown that 96 TWh of electricity could be saved in the highest efficiency scenario due to significant efficiency gains between 2016 and 2050. Further, the results indicate the southeast of the country has the highest potential for consumption reduction: by 27% in the most efficient scenario. This work is the first to use a bottom-up approach to forecast the residential electricity consumption by region and by technology-driven scenarios in Brazil. With this methodology, it is possible to indicate which types of appliances should be targeted by policy makers when designing energy efficiency incentives. Hence, this model can be used as a roadmap to achieve the desired efficiency gains in Brazil.

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Notes

  1. Considering 2016 values (US$ 1.00 = R$ 3.63).

  2. FORECAST (FORecasting Energy Consumption Analysis and Simulation Tool) is a modeling platform that captures the final energy consumption of the industry, residential, tertiary, transport, and agriculture sector (http://www.forecast-model.eu).

References

  • Achão, C., & Schaeffer, R. (2009). Decomposition analysis of the variations in residential electricity consumption in Brazil for the 1980–2007 period: measuring the activity, intensity and structure effects. Energy Policy, 37, 5208–5220.

    Article  Google Scholar 

  • Albers, S. (2004). Forecasting the diffusion of an innovation prior to launch. Cross-functional Innovation Management:, Perspectives from Different Disciplines pp. 243–258.

  • Almeida, M., Schaeffer, R., & La Rovere, E. (2001). The potential for electricity conservation and peak load reduction in the residential sector of Brazil. Energy, 26(4), 413–429. https://doi.org/10.1016/S0360-5442(00)00071-2.

    Article  Google Scholar 

  • Andrade, C., & Pontes, R. (2017). Economic analysis of Brazilian policies for energy efficient electric motors. Energy Policy, 106, 315–325.

    Article  Google Scholar 

  • Bass, F. (1969). A new product growth model for consumer durables. Management Science, 15, 215–217.

    Article  Google Scholar 

  • Braungardt, S., Elsland, R., & Eichhammer, W. (2016). The environmental impact of eco-innovations: the case of EU residential electricity use. Environmental Economics and Policy Studies, 18(2), 213–228. 10.1007/s10018-015-0129-y.

    Article  Google Scholar 

  • Castro, C., Marcato, A. L., Souza, R., Silva Junior, I., Cyrino Oliveira, F. L., & Pulinho, T. (2015). The generation of synthetic inflows via bootstrap to increase the energy efficiency of long-term hydrothermal dispatches. Electric Power Systems Research, 124, 33–46. https://doi.org/10.1016/j.epsr.2015.02.014.

    Article  Google Scholar 

  • CONPET. (2011). Programa nacional da racionalização do uso dos derivados do petróleo e do gás natural [national program for the rationalization of the use of petroleum and natural gas derivatives]. http://www.conpet.gov.br/portal/conpet/pt_br/pagina-inicial.shtml.

  • Cyrino Oliveira, F.L., Ferreira, P.G., & Souza, R. (2014). A parsimonious bootstrap method to model natural inflow energy series, Mathematical Problems in Engineering 2014. https://doi.org/10.1155/2014/158689.

  • Dantas, B. (2014). Estimativa do impacto no consumo de energia causado pelo standby dos aparelhos eletroeletrônicos [estimate of the impact on the energy consumption caused by the standby of the electronic devices]. Ph.D. thesis, Pontifícia Universidade Católica do Rio de Janeiro.

  • Decanio, S. (1997). Modeling technological change in energy demand forecasting: a generalized approach. Technological Forecasting and Social Change, 55, 249–263.

    Article  Google Scholar 

  • EIA. (2016). International energy outlook 2016. Tech. rep., U.S. Energy Information Administration.

  • Elsland, R. (2015). Development of an integrated modelling concept to capture technological myopia: analysing long-term energy demand in the german residential sector. Ph.D. thesis, Karlsruher Instituts für Technologie.

  • Elsland, R., Divrak, C., Fleiter, T., & Wietschel, M. (2014). Turkey’s strategic energy efficiency plan – an ex ante impact assessment of the residential sector. Energy Policy, 70, 14–29.

    Article  Google Scholar 

  • Elsland, R., Eichhammer, W., & Schlomann, B. (2013). Is enough electricity being saved? Impact of energy efficiency policies addressing electrical household appliances in Germany until 2030. In ECEEE SUMMER STUDY PROCEEDINGS (pp. 1651–1662).

  • EPE. (2012). Avaliação da eficiência energética para os próximos 10 anos (2012-2021) [energy efficiency assessment for the next 10 years (2012-2021)]. Tech. rep., Empresa de Pesquisa Energética.

  • EPE. (2016). Estudos da demanda de energia - demanda de energia 2050 [energy demand studies - demand for energy 2050]. Tech. rep., Empresa de Pesquisa Energética, Rio de Janeiro.

  • EPE. (2017). Consumo mensal de energia elétrica por classe (regiões e subsistemas) [monthly consumption of electric energy by class (regions and subsystems)]. http://www.epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/Consumo-.

  • Esteves, G., Maçaira, P., do Carmo, D., Souza, R., Cyrino Oliveira, F.L., & Calili, R. (2016). Long-term scenarios development for the electricity tariffs in Brazil. In 2016 13th International Conference on the European Energy Market (EEM) (pp. 1–6): IEEE, DOI https://doi.org/10.1109/EEM.2016.7521241, (to appear in print).

  • Ferreira, P. G., Cyrino Oliveira, F. L., & Souza, R. (2015). The stochastic effects on the Brazilian electrical sector. Energy Economics, 49, 328–335. https://doi.org/10.1016/j.eneco.2015.03.004.

    Article  Google Scholar 

  • Ghisi, E., Gosch, S., & Lamberts, R. (2007). Electricity end-uses in the residential sector of Brazil. Energy Policy, 35, 4107–4120.

    Article  Google Scholar 

  • Grottera, C., Barbier, C., Sanches-Pereira, A., Abreu, M., Uchôa, C., Tudeschini, L., Cayla, J. M., Nadaud, F., Pereira, Jr A., Cohen, C., & Coelho, S. (2018). Linking electricity consumption of home appliances and standard of living: a comparison between Brazilian and french households. Renewable and Sustainable Energy Reviews, 94, 877–888. https://doi.org/10.1016/j.rser.2018.06.063.

  • Götze, U. (2006). Investitionsrechnung: Modelle und analysen zur beurteilung von investitionsvorhaben [investment calculation: models and analyzes for the assessment of investment projects].

  • Huang, X., Maçaira, P., Hassani, H., Cyrino Oliveira, F. L., & Dhesi, G. (2019). Hydrological natural inflow and climate variables: time and frequency causality analysis. Physica A: Statistical Mechanics and its Applications, 516, 480–495.

    Article  Google Scholar 

  • IBGE. (2013). Projeção da população do do brasil por sexo e idade: 2000-2060 [projection of the Brazilian population by sex and age: 2000-2060]. http://www.ibge.gov.br/home/estatistica/populacao/projecao_da_populacao/2013/.

  • IEA. (2014). Statistics. http://www.iea.org/statistics/.

  • IMF. (2016). World economic outlook database. https://www.imf.org/external/pubs/ft/weo/2016/02/weodata/index.aspx.

  • INMETRO. (2012). Tabelas de consumo/eficiência energética [consumption/energy efficiency tables]. http://www.inmetro.gov.br/consumidor/tabelas.asp.

  • INMETRO. (2017). O programa brasileiro de etiquetagem [the Brazilian labeling program]. http://www2.inmetro.gov.br/pbe/conheca_o_programa.php.

  • IPEA. (2016). Ipeadata. http://www.ipeadata.gov.br/.

  • Jannuzzi, G., & Schipper, L. (1991). The structure of electricity demand in the Brazilian household sector. Energy Policy, 19, 879–891.

    Article  Google Scholar 

  • Keppo, I., & Strubergger, M. (2010). Short term decisions for long term problems – the effect of foresight on model based energy systems analysis. Energy, 35(5), 2033–2042.

    Article  Google Scholar 

  • Kranzl, L., Hummel, M., Mueller, A., & Steinbach, J. (2013). Renewable heating: perspectives and the impact of policy instruments. Energy Policy, 59, 44–58.

    Article  Google Scholar 

  • Levine, J. (2015). Brazil’s water crisis is also an energy crisis. Brazilians themselves are the solution. https://blogs.oracle.com/utilities/brazil-drought-crisis-solution.

  • Lin, B., Ankrah, I., & Manu, S. (2017). Brazilian energy efficiency and energy substitution: a road to cleaner national energy system. Journal of Cleaner Production, 162, 1275–1284.

    Article  Google Scholar 

  • Maçaira, P., Cyrino Oliveira, F.L., Ferreira, P.G., Almeida, F., & Souza, R. (2017). Introducing a causal PAR(p) model to evaluate the influence of climate variables in reservoir inflows: a Brazilian case. Pesquisa Operacional, 37, 107–128. https://doi.org/10.1590/0101-7438.2017.037.01.0107.

    Article  Google Scholar 

  • Maçaira, P., Souza, R., & Cyrino Oliveira, F. L. (2015). Modelling and forecasting the residential electricity consumption in Brazil with Pegels exponential smoothing techniques. In Procedia computer science, vol. 55, DOI https://doi.org/10.1016/j.procs.2015.07.057, (to appear in print).

  • Maçaira, P., Souza, R., & Cyrino Oliveira, F. L. (2016). Forecasting Brazil’s electricity consumption with Pegels exponential smoothing techniques. IEEE Latin America Transactions, 14(3), 1252–1258.

    Article  Google Scholar 

  • McFadden, D. (2013). Conditional logit analysis of qualitative choice behavior, Academic Press, New York.

  • McNeil, N., & Letschert, V. (2010). Modeling diffusion of electrical appliances in the residential sector. Energy and Buildings, 42, 783–790.

    Article  Google Scholar 

  • Melo, C., & Jannuzzi, G. (2010). Energy efficiency standards for refrigerators in Brazil: a methodology for impact evaluation. Energy Policy, 38(11), 6545–6550. 10.1016/j.enpol.2010.07.032.

    Article  Google Scholar 

  • Menezes, M., Souza, R., & Pessanha, J.F. (2015). Electricity consumption forecasting using singular spectrum analysis — previsión del consumo de electricidad mediante análisis spectral singular. DYNA (Colombia) 82(190).

  • MME. (2018). Anuário estatístico de energia elétrica 2018 (ano base 2017) [statistical yearbook for electric energy 2018 (base year 2017)]. Tech. rep., Ministério de Minas e Energia.

  • Mueller, A., & Biermayr, P. (2011). The future of heating demand for space heating and sanitary hot water in austrian buildings until 2025. In Proceedings of the 7. Internationale Energiewirtschaftstagung an der TU Wien (IEWT 2011).

  • Narayan, P., Smyth, R., & Prasad, A. (2007). Electricity consumption in G7 countries: a panel cointegration analysis of residential demand elasticities. Energy Policy, 35, 4485–4494.

    Article  Google Scholar 

  • Oliveira, M., & Rebelatto, D. (2015). The evaluation of electric energy consumption in the Brazilian residential sector: a technological improvement proposal in order to increase its efficiency, Renewable and Sustainable Energy Reviews 49. https://doi.org/10.1016/j.rser.2015.04.069.

  • Pessanha, J.F., & Leon, N. (2012). Long-term forecasting of household and residential electric customers in Brazil. IEEE Latin America Transactions 10(2).

  • Pessanha, J. F., & Leon, N. (2015). Forecasting long-term electricity demand in the residential sector. In Procedia computer science, vol. 55, DOI https://doi.org/10.1016/j.procs.2015.07.032, (to appear in print).

  • PROCEL, & Pesquisa de posse de equipamentos e hábitos de uso, ano base 2005: Classe residencial relatório centro-oeste [equipment ownership and usage habits survey base year 2005: residential class central-west report]. Tech. rep. (2005).

  • PROCEL. (2005). Pesquisa de posse de equipamentos e hábitos de uso, ano base 2005: Classe residencial relatório nordeste [equipment ownership and usage habits survey, base year 2005: residential class northeast report]. Tech. rep., Eletrobras.

  • PROCEL. (2005). Pesquisa de posse de equipamentos e hábitos de uso, ano base 2005: Classe residencial relatório norte [equipment ownership and usage habits survey, base year 2005: residential class north report]. Tech. rep., Eletrobras.

  • PROCEL. (2005). Pesquisa de posse de equipamentos e hábitos de uso, ano base 2005: Classe residencial relatório sudeste [equipment ownership and usage habits survey, base year 2005: residential class southeast report]. Tech. rep., Eletrobras.

  • PROCEL. (2005). Pesquisa de posse de equipamentos e hábitos de uso, ano base 2005: Classe residencial relatório sul [equipment ownership and usage habits survey, base year 2005: residential class south report]. Tech. rep., Eletrobras.

  • PROCEL. (2006). Centro brasileiro de informação de eficiência energética [Brazilian center for energy efficiency information]. http://www.procelinfo.com.br/main.asp.

  • PROCEL. (2015). Resultados procel 2015 - ano base 2014 [procel 2015 results - base year 2014]. Tech. rep., Eletrobras.

  • Satoh, D. (2001). A discrete bass model and its parameter estimation. Journal of the Operations Research Society of Japan, 44(1), 1–18.

    Article  MathSciNet  Google Scholar 

  • Silva, F., Cyrino Oliveira, F. L., & Souza, R. (2019). A bottom-up Bayesian extension for long term electricity consumption forecasting. Energy, 167, 198–210.

    Article  Google Scholar 

  • Silva, F., Souza, R., Cyrino Oliveira, F. L., Lourenço, P., & Calili, R. (2018). A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - application to pulp and paper sector in Brazil. Energy, 144, 1107–1118.

    Article  Google Scholar 

  • Souza, R., Marcato, A. L., Dias, B., & Cyrino Oliveira, F. L. (2012). Optimal operation of hydrothermal systems with hydrological scenario generation through bootstrap and periodic autoregressive models. European Journal of Operational Research, 222, 606–615. https://doi.org/10.1016/j.ejor.2012.05.020.

    Article  Google Scholar 

  • Swan, L., & Ugursal, V. (2009). Modeling of end-use energy consumption in the residential sector: a review of modeling techniques. Renewable and Sustainable Energy Reviews, 13(8), 1819–1835. https://doi.org/10.1016/j.rser.2008.09.033.

    Article  Google Scholar 

  • Torrini, F., Cyrino Oliveira, F. L., Souza, R., & Pessanha, J. F. (2016). Long term electricity consumption forecast in Brazil: a fuzzy logic approach. Socio-Economic Planning Sciences, 1, 18–27.

    Article  Google Scholar 

  • Train, K. (2009). Discrete choice methods with simulation. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  • Tutz, G. (2011). Regression for categorical data. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Villareal, M., & Moreira, J. (2016). Household consumption of electricity in Brazil between 1985 and 2013. Energy Policy, 96, 251–259.

    Article  Google Scholar 

  • Zurn, H., Tenfen, D., Rolim, J., Richter, A., & Hauer, I. (2017). Electrical energy demand efficiency efforts in Brazil, past, lessons learned, present and future: a critical review. Renewable and Sustainable Energy Reviews, 67, 1081–1086. https://doi.org/10.1016/j.rser.2016.09.037.

    Article  Google Scholar 

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Funding

The authors would like to thank the R&D program of the Brazilian Electricity Regulatory Agency (ANEEL) for the financial support (PD-7625-0003/2014). They also thank the Coordination for the Improvement of Higher Education Personnel (CAPES) for the doctoral financial support, and the National Council of Technological and Scientific Development (CNPq) (research project 443595/2014-3) and FAPERJ (research projects E-26/202.806/2015 and E-26/201.912/2015) for their support.

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Authors and Affiliations

  1. Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil

    Paula Maçaira, Fernando Cyrino Oliveira & Reinaldo Souza

  2. Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany

    Rainer Elsland

  3. Fundação Getúlio Vargas, Rio de Janeiro, Brazil

    Gláucia Fernandes

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  1. Paula Maçaira
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  2. Rainer Elsland
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  3. Fernando Cyrino Oliveira
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Correspondence to Paula Maçaira.

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Appendix

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Table 8 Overview of policies and measures addressing energy efficiency in the Brazilian residential sector
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Maçaira, P., Elsland, R., Oliveira, F.C. et al. Forecasting residential electricity consumption: a bottom-up approach for Brazil by region. Energy Efficiency 13, 911–934 (2020). https://doi.org/10.1007/s12053-020-09860-w

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