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Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings

Author

Listed:
  • David Bienvenido-Huertas

    (Department of Building Construction II, University of Seville, 41012 Seville, Spain)

  • Jesús A. Pulido-Arcas

    (Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153-8902, Japan)

  • Carlos Rubio-Bellido

    (Department of Building Construction II, University of Seville, 41012 Seville, Spain)

  • Alexis Pérez-Fargallo

    (Department of Building Science, University of Bío-Bío, Concepción 410300, Chile)

Abstract

In recent times, studies about the accuracy of algorithms to predict different aspects of energy use in the building sector have flourished, being energy poverty one of the issues that has received considerable critical attention. Previous studies in this field have characterized it using different indicators, but they have failed to develop instruments to predict the risk of low-income households falling into energy poverty. This research explores the way in which six regression algorithms can accurately forecast the risk of energy poverty by means of the fuel poverty potential risk index. Using data from the national survey of socioeconomic conditions of Chilean households and generating data for different typologies of social dwellings (e.g., form ratio or roof surface area), this study simulated 38,880 cases and compared the accuracy of six algorithms. Multilayer perceptron, M5P and support vector regression delivered the best accuracy, with correlation coefficients over 99.5%. In terms of computing time, M5P outperforms the rest. Although these results suggest that energy poverty can be accurately predicted using simulated data, it remains necessary to test the algorithms against real data. These results can be useful in devising policies to tackle energy poverty in advance.

Suggested Citation

  • David Bienvenido-Huertas & Jesús A. Pulido-Arcas & Carlos Rubio-Bellido & Alexis Pérez-Fargallo, 2021. "Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings," Sustainability, MDPI, vol. 13(5), pages 1-30, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2426-:d:504773
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    as
    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Hu, Chao & Jain, Gaurav & Zhang, Puqiang & Schmidt, Craig & Gomadam, Parthasarathy & Gorka, Tom, 2014. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Elsevier, vol. 129(C), pages 49-55.
    3. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
    4. Legendre, Bérangère & Ricci, Olivia, 2015. "Measuring fuel poverty in France: Which households are the most fuel vulnerable?," Energy Economics, Elsevier, vol. 49(C), pages 620-628.
    5. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    6. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    7. Fabbri, Kristian, 2015. "Building and fuel poverty, an index to measure fuel poverty: An Italian case study," Energy, Elsevier, vol. 89(C), pages 244-258.
    8. Scarpellini, Sabina & Rivera-Torres, Pilar & Suárez-Perales, Inés & Aranda-Usón, Alfonso, 2015. "Analysis of energy poverty intensity from the perspective of the regional administration: Empirical evidence from households in southern Europe," Energy Policy, Elsevier, vol. 86(C), pages 729-738.
    9. Afsarian, Fatemeh & Saber, Aniseh & Pourzangbar, Ali & Olabi, Abdul Ghani & Khanmohammadi, Mohammad Ali, 2018. "Analysis of recycled aggregates effect on energy conservation using M5′ model tree algorithm," Energy, Elsevier, vol. 156(C), pages 264-277.
    10. Thomson, Harriet & Snell, Carolyn, 2013. "Quantifying the prevalence of fuel poverty across the European Union," Energy Policy, Elsevier, vol. 52(C), pages 563-572.
    11. Bienvenido-Huertas, David & Moyano, Juan & Rodríguez-Jiménez, Carlos E. & Marín, David, 2019. "Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method," Applied Energy, Elsevier, vol. 233, pages 1-14.
    12. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    13. Azofra, D. & Martínez, E. & Jiménez, E. & Blanco, J. & Azofra, F. & Saenz-Díez, J.C., 2015. "Comparison of the influence of photovoltaic and wind power on the Spanish electricity prices by means of artificial intelligence techinques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 532-542.
    14. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2018. "Artificial neural networks and linear regression prediction models for social housing allocation: Fuel Poverty Potential Risk Index," Energy, Elsevier, vol. 164(C), pages 627-641.
    15. Li, Chuan & Tao, Ying & Ao, Wengang & Yang, Shuai & Bai, Yun, 2018. "Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition," Energy, Elsevier, vol. 165(PB), pages 1220-1227.
    16. Matthias Braubach & Arnaud Ferrand, 2013. "Energy efficiency, housing, equity and health," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 58(3), pages 331-332, June.
    17. Narula R. & Prasad T., 2013. "The growth of outward FDI and the competitiveness : the case of India," MERIT Working Papers 2013-042, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    18. Legendre, Bérangère & Ricci, Olivia, 2015. "Measuring fuel poverty in France: Which households are the most fuel vulnerable?," Energy Economics, Elsevier, vol. 49(C), pages 620-628.
    19. Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A. & Javier Guevara-García, Fco., 2018. "Fuel Poverty Potential Risk Index in the context of climate change in Chile," Energy Policy, Elsevier, vol. 113(C), pages 157-170.
    20. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
    21. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
    22. Raffaele Miniaci & Carlo Scarpa & Paola Valbonesi, 2014. "Fuel poverty and the energy benefits system: The Italian case," IEFE Working Papers 66, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.
    23. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    24. Okushima, Shinichiro, 2017. "Gauging energy poverty: A multidimensional approach," Energy, Elsevier, vol. 137(C), pages 1159-1166.
    25. Schuessler, Rudolf, 2014. "Energy poverty indicators: Conceptual issues. Part I: The ten-percent-rule and double median/mean indicators," ZEW Discussion Papers 14-037, ZEW - Leibniz Centre for European Economic Research.
    26. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
    27. Rosenow, Jan & Platt, Reg & Flanagan, Brooke, 2013. "Fuel poverty and energy efficiency obligations – A critical assessment of the supplier obligation in the UK," Energy Policy, Elsevier, vol. 62(C), pages 1194-1203.
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    1. Spandagos, Constantine & Tovar Reaños, Miguel & Lynch, Muireann Á, 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Papers WP762, Economic and Social Research Institute (ESRI).

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