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Data Envelopment Analysis for Energy Audits of Housing Properties

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1525))

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Abstract

Energy audit is a standardized and well-accepted process, consisting of inspection, survey, assessment, and evaluation of residential properties, commercial establishments and industrial plants, using sophisticated instruments as well as human experts. Energy audit of a city consists of inspections of many thousands (or even hundreds of thousands) of properties of varied ages, types, sizes, conditions, occupancy, and usage patterns. In this paper, we demonstrate how data envelopment analysis (DEA) techniques can be used to derive useful insights from energy audit data of a city, as compared to regression-based or anomaly detection based approaches. We also show how DEA can be used to come up with recommendations for reducing energy consumption. We illustrate the approach by analyzing energy audit data of Austin, Texas.

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Notes

  1. 1.

    https://www.iea.org/reports/global-energy-review-2021.

  2. 2.

    cran.r-project.org/web/packages/Benchmarking/index.html.

  3. 3.

    cran.r-project.org/web/packages/deaR/index.html.

  4. 4.

    https://austinenergy.com/ae/energy-efficiency/ecad-ordinance/for-multifamily-properties.

References

  1. Krarti, M.: Energy Audit of Building Systems: An Engineering Approach. CRC Press, Boca Raton (2000)

    Google Scholar 

  2. Thumann, A.: Handbook of Energy Audits, 9th edn. The Fairmont Press, Lilburn (2012)

    Google Scholar 

  3. Mardani, A., Zavadskas, E.K., Streimikiene, D., Jusoh, A., Khoshnoudi, M.: A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renew. Sustain. Energy Rev. 70, 1298–1322 (2017)

    Google Scholar 

  4. Xu, T., You, J., Li, H., Shao, L.: Energy efficiency evaluation based on data envelopment analysis: a literature review. Energies 13(3548) (2020)

    Google Scholar 

  5. Arens, A.A., Loebbecke, J.K.: Auditing: An Integrated Approach, 8th edn. Pearson, London (1999)

    Google Scholar 

  6. Dickey, G., Blanke, S., Seaton, L.: Machine learning in auditing: current and future applications. CPA J. 89(6), 16–21 (2019)

    Google Scholar 

  7. Bowling, S., Meyer, C.: How we successfully implemented AI in audit. J. Account. 227(5), 26–28 (2019)

    Google Scholar 

  8. Kokina, J., Davenport, T.H.: The emergence of artificial intelligence: how automation is changing auditing. J. Emerg. Technol. Account. 14(1), 115–122 (2017)

    Article  Google Scholar 

  9. Issa, H., Sun, T., Vasarhelyi, M.A.: Research ideas for artificial intelligence in auditing: the formalization of audit and workforce supplementation. J. Emerg. Technol. Account. 13(2), 1–20 (2016)

    Article  Google Scholar 

  10. Brown-Liburd, H., Issa, H., Lombardi, D.: Behavioral implications of big data’s impact on audit judgment and decision making and future research directions. Account. Horiz. 29(2), 451–468 (2015)

    Article  Google Scholar 

  11. Earley, C.E.: Data Analytics in Auditing: Opportunities and Challenges, vol. 58 (2015)

    Google Scholar 

  12. Issa, H., Kogan, A.: A predictive ordered logistic regression model as a tool for quality review of control risk assessments. J. Inf. Syst. 28(2), 209–229 (2014)

    Google Scholar 

  13. Hunton, J.E., Rose, J.M.: 21st-century auditing: advancing decision support systems to achieve continuous auditing. Account. Horiz. 24(2), 297–312 (2010)

    Article  Google Scholar 

  14. Omoteso, K., Patel, A., Scott, P.: Information and communications technology and auditing: current implications and future directions. Int. J. Audit. 14(2), 147–162 (2010)

    Google Scholar 

  15. Etheridge, H.L., Sriram, R.S., Hsu, H.K.: A comparison of selected artificial neural networks that help auditors evaluate client financial viability. Decis. Sci. 31(2), 531–550 (2000)

    Article  Google Scholar 

  16. Asarhelyi, M.A.: Artificial intelligence in accounting and auditing: the use of expert systems (1989)

    Google Scholar 

  17. Seyedzadeh, S., Rahimian, F.P., Glesk, I., Roper, M.: Machine learning for estimation of building energy consumption and performance: a review. Visual. Eng. 6(1), 1–20 (2018)

    Google Scholar 

  18. Beccali, M., Ciulla, G., Brano, V.L., Galatioto, A., Bonomolo, M.: Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in southern Italy. Energy 137, 1201–1218 (2017)

    Google Scholar 

  19. Lara, R.A., Pernigotto, G., Cappelletti, F., Romagnoni, P., Gasparella, A.: Energy audit of schools by means of cluster analysis. Energy Build. 95, 160–171 (2015)

    Google Scholar 

  20. Marasco, D.E., Kontokosta, C.E.: Applications of machine learning methods to identifying and predicting building retrofit opportunities. Energy Build. 128, 431–441 (2016)

    Google Scholar 

  21. Guo, X.-D., Zhu, L., Fan, Y., Xie, B.-C.: Evaluation of potential reductions in carbon emissions in Chinese Provinces based on environmental DEA. Energy Policy 39(5), 2352–2360 (2011)

    Article  Google Scholar 

  22. Picazo-Tadeo, A.J., Beltrán-Esteve, M., Gómez-Limón, J.A.: Assessing eco-efficiency with directional distance functions. Eur. J. Oper. Res. 220(3), 798–809 (2012)

    Google Scholar 

  23. Zhou, P., Ang, B.W., Han, J.Y.: Total factor carbon emission performance: a Malmquist index analysis. Energy Econ. 32(1), 194–201 (2010)

    Google Scholar 

  24. Hu, J.-L., Kao, C.-H.: Efficient energy-saving targets for APEC economies. Energy Policy 35(1), 373–382 (2007)

    Article  Google Scholar 

  25. Song, M., An, Q., Zhang, W., Wang, Z., Jie, W.: Environmental efficiency evaluation based on data envelopment analysis: a review. Renew. Sustain. Energy Rev. 16(7), 4465–4469 (2012)

    Article  Google Scholar 

  26. Hu, J.-L., Wang, S.-C.: Total-factor energy efficiency of regions in China. Energy Policy 34(17), 3206–3217 (2006)

    Article  Google Scholar 

  27. He, P., Sun, Y., Shen, H., Jian, J., Zhongfu, Y.: Does environmental tax affect energy efficiency? An empirical study of energy efficiency in OECD countries based on DEA and logit model. Sustainability 11(14), 3792 (2019)

    Article  Google Scholar 

  28. Liu, Y., Wang, K.: Energy efficiency of China’s industry sector: an adjusted network DEA (data envelopment analysis)-based decomposition analysis. Energy 93, 1328–1337 (2015)

    Article  Google Scholar 

  29. Färe, R., Grosskopf, S., Lindgren, B., Roos, P.: Productivity developments in Swedish hospitals: a Malmquist output index approach. In: Charnes, W.W.C., Lewin, A.Y., Seiford, L.M. (eds.) Data Envelopment Analysis: Theory, Methodology, and Applications, pp. 253–272. Springer, Dordrecht (1994). https://doi.org/10.1007/978-94-011-0637-5_13

    Chapter  Google Scholar 

  30. Rhodes, J.D., Stephens, B., Webber, M.E.: Using energy audits to investigate the impacts of common air-conditioning design and installation issues on peak power demand and energy consumption in Austin, Texas. Energy Build. 43(11), 3271–3278 (2011)

    Google Scholar 

  31. Rhodes, J., Stephens, B., Webber, M.E.: Energy audit analysis of residential air-conditioning systems in Austin, Texas. ASHRAE Trans. 118(1) (2012)

    Google Scholar 

  32. Aparicio, J.: A survey on measuring efficiency through the determination of the least distance in data envelopment analysis. J. Centrum Cathedra (2016)

    Google Scholar 

  33. Bogetoft, P., Otto, L.: Benchmarking with DEA, SFA, and R, vol. 157. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-7961-2

    Book  MATH  Google Scholar 

  34. Tone, K.: A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130(3), 498–509 (2001)

    Article  MathSciNet  Google Scholar 

  35. Amirteimoori, A., Kordrostami, S.: A Euclidean distance-based measure of efficiency in data envelopment analysis. Optimization 59(7), 985–996 (2010)

    Article  MathSciNet  Google Scholar 

  36. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)

    Article  MathSciNet  Google Scholar 

  37. Cooper, W.W., Seiford, L.M., Tone, K.: Data Envelopment Analysis, 2nd edn. Springer, Boston (2007). https://doi.org/10.1007/b109347

    Book  MATH  Google Scholar 

  38. Zhao, Y., Nasrullah, Z., Li, Z.: PyOD: a Python toolbox for scalable outlier detection. J. Mach. Learn. Res. 20(96), 1–7 (2019)

    Google Scholar 

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Authors and Affiliations

  1. TCS Research, Tata Consultancy Services Ltd., Pune, 411013, India

    Sushodhan Vaishampayan, Aditi Pawde, Akshada Shinde, Manoj Apte & Girish Keshav Palshikar

Authors
  1. Sushodhan Vaishampayan
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  2. Aditi Pawde
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  3. Akshada Shinde
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  4. Manoj Apte
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  5. Girish Keshav Palshikar
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Correspondence to Sushodhan Vaishampayan .

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  1. IKIM, Ruhr-University Bochum, Bochum, Germany

    Michael Kamp

  2. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

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    Adrien Bibal

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Vaishampayan, S., Pawde, A., Shinde, A., Apte, M., Palshikar, G.K. (2021). Data Envelopment Analysis for Energy Audits of Housing Properties. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_40

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  • DOI: https://doi.org/10.1007/978-3-030-93733-1_40

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