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Applying models for ordinal logistic regression to the analysis of household electricity consumption classes in Rio de Janeiro, Brazil

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  • Fuks, Mauricio
  • Salazar, Esther

Abstract

This study applies the proportional odds and partial proportional odds models for ordinal logistic regression to analyze household electricity consumption classes. Micro-data from households situated in the state of Rio de Janeiro during 2004 was used to measure the performance of the models in correctly classifying household electricity consumption classes via sociodemographic, electricity usage and dwelling characteristics. The strategy of using binary logistic regressions to test the main hypothesis of the proportional odds model, suggested by Bender and Grouven, was successful in identifying which of the independent variables could be estimated via the proportional odds assumption. Results indicate that the partial proportional odds models is slightly superior to the more restrictive approach. The study includes probabilistic examples to describe how changes in the independent variables affect the probability of a household belonging to specific classes of electricity consumption. Projections using the final model indicated that the approach may be useful for estimating aggregate household electricity consumption.

Suggested Citation

  • Fuks, Mauricio & Salazar, Esther, 2008. "Applying models for ordinal logistic regression to the analysis of household electricity consumption classes in Rio de Janeiro, Brazil," Energy Economics, Elsevier, vol. 30(4), pages 1672-1692, July.
    • Handle: RePEc:eee:eneeco:v:30:y:2008:i:4:p:1672-1692
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    References listed on IDEAS

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    2. Verdejo, Humberto & Awerkin, Almendra & Saavedra, Eugenio & Kliemann, Wolfgang & Vargas, Luis, 2016. "Stochastic modeling to represent wind power generation and demand in electric power system based on real data," Applied Energy, Elsevier, vol. 173(C), pages 283-295.
    3. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2012. "Estimation of elasticity price of electricity with incomplete information," Energy Economics, Elsevier, vol. 34(3), pages 627-633.
    4. Chen, Peipei & Wu, Yi & Zhong, Honglin & Long, Yin & Meng, Jing, 2022. "Exploring household emission patterns and driving factors in Japan using machine learning methods," Applied Energy, Elsevier, vol. 307(C).
    5. Ozcam, Ahmet & Karadeniz, Esra, 2012. "The Determinants of the Growth Expectations of Turkish Entrepreneurs in the Way up the Entrepreneural Ladder Using Ordinal Logistic Model (OLM)," MPRA Paper 49908, University Library of Munich, Germany.
    6. Álvaro L. Ferreira & Tomás C. de Castro & Marcelo A. Costa & Sérgio H. R. Ribeiro & Iguatinan G. Monteiro, 2023. "Financial sustainability disparities among energy distribution companies: a multi-factor study case in Brazil," SN Business & Economics, Springer, vol. 3(7), pages 1-35, July.
    7. Goto, Hisanori & Goto, Mika & Sueyoshi, Toshiyuki, 2011. "Consumer choice on ecologically efficient water heaters: Marketing strategy and policy implications in Japan," Energy Economics, Elsevier, vol. 33(2), pages 195-208, March.
    8. Verdejo, Humberto & Awerkin, Almendra & Becker, Cristhian & Olguin, Gabriel, 2017. "Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 512-521.
    9. Yi-Tui Chen, 2017. "The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    10. Ribeiro, Fernando & Ferreira, Paula & Araújo, Madalena & Braga, Ana Cristina, 2018. "Modelling perception and attitudes towards renewable energy technologies," Renewable Energy, Elsevier, vol. 122(C), pages 688-697.

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