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Simulating Brazilian Electricity Demand Under Climate Change Scenarios

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  • Trotter, Ian Michael
  • Féres, José Gustavo
  • Bolkesjø, Torjus Folsland
  • de Hollanda, Lavínia Rocha

Abstract

Long-term load forecasts are important for planning the development of the electric power infrastructure. We present a methodology for simulating ensembles of daily long-term load forecasts for Brazil under climate change scenarios. For certain applications, it is important to choose an ensemble approach in order to estimate the (conditional) probability distribution of the load. High temporal resolution is necessary in order to preserve key features of the electricity demand that are particularly important in the face of increasing penetration of intermittent renewable power generation.

Suggested Citation

  • Trotter, Ian Michael & Féres, José Gustavo & Bolkesjø, Torjus Folsland & de Hollanda, Lavínia Rocha, 2015. "Simulating Brazilian Electricity Demand Under Climate Change Scenarios," Working Papers in Applied Economics 208689, Universidade Federal de Vicosa, Departamento de Economia Rural.
  • Handle: RePEc:ags:ufvdwp:208689
    DOI: 10.22004/ag.econ.208689
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    1. Zachariadis, Theodoros & Hadjinicolaou, Panos, 2014. "The effect of climate change on electricity needs – A case study from Mediterranean Europe," Energy, Elsevier, vol. 76(C), pages 899-910.
    2. Brian O’Neill & Elmar Kriegler & Keywan Riahi & Kristie Ebi & Stephane Hallegatte & Timothy Carter & Ritu Mathur & Detlef Vuuren, 2014. "A new scenario framework for climate change research: the concept of shared socioeconomic pathways," Climatic Change, Springer, vol. 122(3), pages 387-400, February.
    3. Tao Hong & Jason Wilson & Jingrui Xie, 2013. "Long term probabilistic load forecasting and normalization with hourly information," HSC Research Reports HSC/13/13, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
    5. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
    6. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
    7. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    8. B.C. O'Neill & T Carter & Kl Ebi & J. Edmonds & Stéphane Hallegatte & E. Kemp-Benedict & E. Kriegler & L. Mearns & R. Moss & K. Riahi & B. van Ruijven & D. van Vuuren, 2012. "Meeting Report of the Workshop on The Nature and Use of New Socioeconomic Pathways for Climate Change Research," CIRED Working Papers hal-00801931, HAL.
    9. Uri, Noel D., 1978. "Forecasting peak system load using a combined time series and econometric model," Applied Energy, Elsevier, vol. 4(3), pages 219-227, July.
    10. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.
    11. Ferreira, Pedro Guilherme Costa & Oliveira, Fernando Luiz Cyrino & Souza, Reinaldo Castro, 2015. "The stochastic effects on the Brazilian Electrical Sector," Energy Economics, Elsevier, vol. 49(C), pages 328-335.
    12. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    13. Adams, Gail & Allen, P. Geoffrey & Morzuch, Bernard J., 1991. "Probability distributions of short-term electricity peak load forecasts," International Journal of Forecasting, Elsevier, vol. 7(3), pages 283-297, November.
    14. Taylor, James W. & Buizza, Roberto, 2003. "Using weather ensemble predictions in electricity demand forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 57-70.
    15. Yee Yan, Yuk, 1998. "Climate and residential electricity consumption in Hong Kong," Energy, Elsevier, vol. 23(1), pages 17-20.
    16. Kaufmann, Robert K. & Gopal, Sucharita & Tang, Xiaojing & Raciti, Steve M. & Lyons, Paul E. & Geron, Nick & Craig, Francis, 2013. "Revisiting the weather effect on energy consumption: Implications for the impact of climate change," Energy Policy, Elsevier, vol. 62(C), pages 1377-1384.
    17. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    18. Pielow, Amy & Sioshansi, Ramteen & Roberts, Matthew C., 2012. "Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors," Energy, Elsevier, vol. 46(1), pages 533-540.
    19. Horowitz, Shira & Mauch, Brandon & Sowell, Fallaw, 2014. "Forecasting residential air conditioning loads," Applied Energy, Elsevier, vol. 132(C), pages 47-55.
    20. Schaeffer, Roberto & Szklo, Alexandre Salem & Pereira de Lucena, André Frossard & Moreira Cesar Borba, Bruno Soares & Pupo Nogueira, Larissa Pinheiro & Fleming, Fernanda Pereira & Troccoli, Alberto & , 2012. "Energy sector vulnerability to climate change: A review," Energy, Elsevier, vol. 38(1), pages 1-12.
    21. Harris, John L. & Liu, Lon-Mu, 1993. "Dynamic structural analysis and forecasting of residential electricity consumption," International Journal of Forecasting, Elsevier, vol. 9(4), pages 437-455, December.
    22. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    23. Badurally Adam, N.R. & Elahee, M.K. & Dauhoo, M.Z., 2011. "Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process," Energy, Elsevier, vol. 36(12), pages 6763-6769.
    24. Rob J Hyndman & Shu Fan, 2008. "Density forecasting for long-term peak electricity demand," Monash Econometrics and Business Statistics Working Papers 6/08, Monash University, Department of Econometrics and Business Statistics.
    25. Mideksa, Torben K. & Kallbekken, Steffen, 2010. "The impact of climate change on the electricity market: A review," Energy Policy, Elsevier, vol. 38(7), pages 3579-3585, July.
    26. De Felice, Matteo & Alessandri, Andrea & Catalano, Franco, 2015. "Seasonal climate forecasts for medium-term electricity demand forecasting," Applied Energy, Elsevier, vol. 137(C), pages 435-444.
    27. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    28. Detlef Vuuren & Jae Edmonds & Mikiko Kainuma & Keywan Riahi & Allison Thomson & Kathy Hibbard & George Hurtt & Tom Kram & Volker Krey & Jean-Francois Lamarque & Toshihiko Masui & Malte Meinshausen & N, 2011. "The representative concentration pathways: an overview," Climatic Change, Springer, vol. 109(1), pages 5-31, November.
    29. Kristie Ebi & Stephane Hallegatte & Tom Kram & Nigel Arnell & Timothy Carter & Jae Edmonds & Elmar Kriegler & Ritu Mathur & Brian O’Neill & Keywan Riahi & Harald Winkler & Detlef Vuuren & Timm Zwickel, 2014. "A new scenario framework for climate change research: background, process, and future directions," Climatic Change, Springer, vol. 122(3), pages 363-372, February.
    30. Detlef Vuuren & Elmar Kriegler & Brian O’Neill & Kristie Ebi & Keywan Riahi & Timothy Carter & Jae Edmonds & Stephane Hallegatte & Tom Kram & Ritu Mathur & Harald Winkler, 2014. "A new scenario framework for Climate Change Research: scenario matrix architecture," Climatic Change, Springer, vol. 122(3), pages 373-386, February.
    31. Takenawa, Tadashi & Schneider, Alan M. & Schiffman, Dean A., 1980. "A computer program for 24-hour electric utility load forecasting," Energy, Elsevier, vol. 5(7), pages 571-585.
    32. Lora Dos Anjos Rodrigues & José Gustavo Féres & Leonardo Bornacki De Matto, 2014. "Aquecimento Global E A Demanda Residencial De Energia Elétrica No Brasil," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41st Brazilian Economics Meeting] 201, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    33. Bartels, Robert & Fiebig, Denzil G. & Garben, Michael & Lumsdaine, Robert, 1992. "An end-use electricity load simulation model : Delmod," Utilities Policy, Elsevier, vol. 2(1), pages 71-82, January.
    34. Sandels, C. & Widén, J. & Nordström, L., 2014. "Forecasting household consumer electricity load profiles with a combined physical and behavioral approach," Applied Energy, Elsevier, vol. 131(C), pages 267-278.
    35. Zachariadis, Theodoros, 2010. "Forecast of electricity consumption in Cyprus up to the year 2030: The potential impact of climate change," Energy Policy, Elsevier, vol. 38(2), pages 744-750, February.
    36. Elmar Kriegler & Jae Edmonds & Stéphane Hallegatte & Kristie Ebi & Tom Kram & Keywan Riahi & Harald Winkler & Detlef Vuuren, 2014. "A new scenario framework for climate change research: the concept of shared climate policy assumptions," Climatic Change, Springer, vol. 122(3), pages 401-414, February.
    37. Beccali, M. & Cellura, M. & Lo Brano, V. & Marvuglia, A., 2008. "Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(8), pages 2040-2065, October.
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    Cited by:

    1. Trotter, Ian M. & Bolkesjø, Torjus Folsland & Féres, José Gustavo & Hollanda, Lavinia, 2016. "Climate change and electricity demand in Brazil: A stochastic approach," Energy, Elsevier, vol. 102(C), pages 596-604.

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    Keywords

    Demand and Price Analysis; Environmental Economics and Policy; Resource /Energy Economics and Policy; Risk and Uncertainty;
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