IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v102y2016icp596-604.html
   My bibliography  Save this article

Climate change and electricity demand in Brazil: A stochastic approach

Author

Listed:
  • Trotter, Ian M.
  • Bolkesjø, Torjus Folsland
  • Féres, José Gustavo
  • Hollanda, Lavinia

Abstract

We present a framework for incorporating weather uncertainty into electricity demand forecasting when weather patterns cannot be assumed to be stable, such as in climate change scenarios. This is done by first calibrating an econometric model for electricity demand on historical data, and subsequently applying the model to a large number of simulated weather paths, together with projections for the remaining determinants. Simulated weather paths are generated based on output from a global circulation model, using a method that preserves the trend and annual seasonality of the first and second moments, as well as the spatial and serial correlations. The application of the framework is demonstrated by creating long-term, probabilistic electricity demand forecasts for Brazil for the period 2016–2100 that incorporates weather uncertainty for three climate change scenarios. All three scenarios indicate steady growth in annual average electricity demand until reaching a peak of approximately 1071–1200 TWh in 2060, then subsequently a decline, largely reflecting the trajectory of the population projections. The weather uncertainty in all scenarios is significant, with up to 400 TWh separating the 10th and the 90th percentiles, or approximately ±17% relative to the mean.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:102:y:2016:i:c:p:596-604
    DOI: 10.1016/j.energy.2016.02.120
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216301827
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2016.02.120?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    3. 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.
    4. 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.
    5. 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.
    6. Pilli-Sihvola, Karoliina & Aatola, Piia & Ollikainen, Markku & Tuomenvirta, Heikki, 2010. "Climate change and electricity consumption--Witnessing increasing or decreasing use and costs?," Energy Policy, Elsevier, vol. 38(5), pages 2409-2419, May.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Uri, Noel D, 1977. "Forecasting: A hybrid approach," Omega, Elsevier, vol. 5(4), pages 463-472.
    12. 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.
    13. 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.
    14. 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.
    15. C.J. Ziser & Z.Y. Dong & K.P. Wong, 2012. "Incorporating weather uncertainty in demand forecasts for electricity market planning," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(7), pages 1336-1346.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tian, Chuyin & Huang, Guohe & Piwowar, Joseph M. & Yeh, Shin-Cheng & Lu, Chen & Duan, Ruixin & Ren, Jiayan, 2022. "Stochastic RCM-driven cooling and heating energy demand analysis for residential building," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    2. Tian, Chuyin & Huang, Guohe & Lu, Chen & Zhou, Xiong & Duan, Ruixin, 2021. "Development of enthalpy-based climate indicators for characterizing building cooling and heating energy demand under climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    3. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    4. Ian M. Trotter & Lu'is A. C. Schmidt & Bruno C. M. Pinto & Andrezza L. Batista & J'essica Pellenz & Maritza Isidro & Aline Rodrigues & Attawan G. S. Suela & Loredany Rodrigues, 2020. "COVID-19 and Global Economic Growth: Policy Simulations with a Pandemic-Enabled Neoclassical Growth Model," Papers 2005.13722, arXiv.org, revised Jun 2020.
    5. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
    6. Mauree, Dasaraden & Naboni, Emanuele & Coccolo, Silvia & Perera, A.T.D. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 733-746.
    7. João Vitor Leme & Wallace Casaca & Marilaine Colnago & Maurício Araújo Dias, 2020. "Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models," Energies, MDPI, vol. 13(6), pages 1-20, March.
    8. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    9. Ian M. Trotter & Torjus F. Bolkesj{o} & Eirik O. J{aa}stad & Jon Gustav Kirkerud, 2021. "Increased Electrification of Heating and Weather Risk in the Nordic Power System," Papers 2112.02893, arXiv.org.
    10. Fan, Jing-Li & Hu, Jia-Wei & Zhang, Xian, 2019. "Impacts of climate change on electricity demand in China: An empirical estimation based on panel data," Energy, Elsevier, vol. 170(C), pages 880-888.
    11. Kang, Jieyi & Reiner, David M., 2022. "What is the effect of weather on household electricity consumption? Empirical evidence from Ireland," Energy Economics, Elsevier, vol. 111(C).
    12. Hsiao, Cody Yu-Ling & Chen, Hsing Hung, 2018. "The contagious effects on economic development after resuming construction policy for nuclear power plants in Coastal China," Energy, Elsevier, vol. 152(C), pages 291-302.
    13. Huntington, Hillard G. & Barrios, James J. & Arora, Vipin, 2019. "Review of key international demand elasticities for major industrializing economies," Energy Policy, Elsevier, vol. 133(C).
    14. Yang, Shubo & Jahanger, Atif & Awan, Ashar, 2024. "Temperature variation and urban electricity consumption in China: Implications for demand management and planning," Utilities Policy, Elsevier, vol. 90(C).
    15. Atalla, Tarek & Gualdi, Silvio & Lanza, Alessandro, 2018. "A global degree days database for energy-related applications," Energy, Elsevier, vol. 143(C), pages 1048-1055.
    16. Ang, B.W. & Wang, H. & Ma, Xiaojing, 2017. "Climatic influence on electricity consumption: The case of Singapore and Hong Kong," Energy, Elsevier, vol. 127(C), pages 534-543.
    17. Zhanyang Xu & Jian Xu & Chengxi Xu & Hong Zhao & Hongyan Shi & Zhe Wang, 2024. "Analysis of the Impact of Policies and Meteorological Factors on Industrial Electricity Demand in Jiangsu Province," Sustainability, MDPI, vol. 16(22), pages 1-23, November.
    18. Zheng, Shuguang & Huang, Guohe & Zhou, Xiong & Zhu, Xiaohang, 2020. "Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 261(C).
    19. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
    20. Ahmed, T. & Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2018. "Load forecasting under changing climatic conditions for the city of Sydney, Australia," Energy, Elsevier, vol. 142(C), pages 911-919.
    21. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    22. Jose M. Garrido-Perez & David Barriopedro & Ricardo García-Herrera & Carlos Ordóñez, 2021. "Impact of climate change on Spanish electricity demand," Climatic Change, Springer, vol. 165(3), pages 1-18, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Enrica De Cian & Ian Sue Wing, 2016. "Global Energy Demand in a Warming Climate," Working Papers 2016.16, Fondazione Eni Enrico Mattei.
    3. Angel Manuel Benitez Rodriguez & Ian Michael Trotter, 2019. "Climate change scenarios for Paraguayan power demand 2017–2050," Climatic Change, Springer, vol. 156(3), pages 425-445, October.
    4. Enrica Cian & Ian Sue Wing, 2019. "Global Energy Consumption in a Warming Climate," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(2), pages 365-410, February.
    5. Roson, Roberto & Damania, Richard, 2016. "Simulating the Macroeconomic Impact of Future Water Scarcity an Assessment of Alternative Scenarios," Conference papers 332687, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    6. Juliette N. Rooney-Varga & Florian Kapmeier & John D. Sterman & Andrew P. Jones & Michele Putko & Kenneth Rath, 2020. "The Climate Action Simulation," Simulation & Gaming, , vol. 51(2), pages 114-140, April.
    7. Jerome Dumortier & Miguel Carriquiry & Amani Elobeid, 2021. "Impact of climate change on global agricultural markets under different shared socioeconomic pathways," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 963-984, November.
    8. Osamu Nishiura & Makoto Tamura & Shinichiro Fujimori & Kiyoshi Takahashi & Junya Takakura & Yasuaki Hijioka, 2020. "An Assessment of Global Macroeconomic Impacts Caused by Sea Level Rise Using the Framework of Shared Socioeconomic Pathways and Representative Concentration Pathways," Sustainability, MDPI, vol. 12(9), pages 1-12, May.
    9. Alison Rothwell & Brad Ridoutt & William Bellotti, 2016. "Greenhouse Gas Implications of Peri-Urban Land Use Change in a Developed City under Four Future Climate Scenarios," Land, MDPI, vol. 5(4), pages 1-23, December.
    10. Parinaz Rashidi & Sopan D. Patil & Aafke M. Schipper & Rob Alkemade & Isabel Rosa, 2023. "Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium," Land, MDPI, vol. 12(9), pages 1-19, September.
    11. Milan Ščasný & Emanuele Massetti & Jan Melichar & Samuel Carrara, 2015. "Quantifying the Ancillary Benefits of the Representative Concentration Pathways on Air Quality in Europe," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 383-415, October.
    12. Kılkış, Şiir, 2024. "Urban emissions and land use efficiency scenarios for avoiding increments of global warming," Energy, Elsevier, vol. 307(C).
    13. Food and Agriculture Organization of the United Nations (FAO), "undated". "The future of food and agriculture – Alternative pathways to 2050," The Future of Food and Agriculture 319842, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    14. O'Neill, Brian, 2016. "The Shared Socioeconomic Pathways (SSPs) and their extension and use in impact, adaptation and vulnerability studies," Conference papers 332808, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    15. Roberto Roson, 2017. "Simulating the Macroeconomic Impact of Future Water Scarcity," World Bank Publications - Reports 26027, The World Bank Group.
    16. P. Marcos-Garcia & M. Pulido-Velazquez & C. Sanchis-Ibor & M. García-Mollá & M. Ortega-Reig & A. Garcia-Prats & C. Girard, 2023. "From local knowledge to decision making in climate change adaptation at basin scale. Application to the Jucar River Basin, Spain," Climatic Change, Springer, vol. 176(4), pages 1-23, April.
    17. Koundouri, Phoebe & Papayiannis, Georgios I. & Vassilopoulos, Achilleas & Yannacopoulos, Athanasios N., 2023. "Probabilistic Scenario-Based Assessment of National Food Security Risks with Application to Egypt and Ethiopia," MPRA Paper 122007, University Library of Munich, Germany.
    18. Roberto Roson & Richard Damania, the World Bank, Washington D.C., 2016. "Simulating the Macroeconomic Impact of Future Water Scarcity," EcoMod2016 9167, EcoMod.
    19. Vanessa J. Schweizer, 2020. "Reflections on cross-impact balances, a systematic method constructing global socio-technical scenarios for climate change research," Climatic Change, Springer, vol. 162(4), pages 1705-1722, October.
    20. Vivek Srikrishnan & Yawen Guan & Richard S. J. Tol & Klaus Keller, 2022. "Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model," Climatic Change, Springer, vol. 170(3), pages 1-20, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:102:y:2016:i:c:p:596-604. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.