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Predicting total household energy expenditures using ensemble learning methods

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  • Kesriklioğlu, Esma
  • Oktay, Erkan
  • Karaaslan, Abdulkerim

Abstract

Total household energy expenditures are a complex topic because so many behavioral, technological, environmental, and policy variables can affect expenditures. This study aimed to develop a high-performance ensemble learning (EL) model to classify total household energy expenditures. For this purpose, household consumption data from 11,521 households were examined using the Household Budget Survey 2019 data set that the Turkish Statistical Institute (TURKSTAT) published. In addition to the variables directly related to household energy expenditures, new variables were created within the framework of the literature and under the guidance of expert opinion. The prepared data were passed through data preprocessing, modeling, prediction, and performance evaluation stages using the open source RapidMiner software program. Classification performances of machine learning and EL methods were compared. Aside from k-nearest neighbor, decision tree, naive Bayes, random forest, gradient boosted trees, and DFNN classifiers, the study used bagging, boosting, voting, and stacking EL methods. The stacking EL method in the ALL model and bagging EL method in the deep feed forward neural network (DFNN) classifiers achieved the highest performance among EL methods. The accuracy value of the stacking and bagging methods was 0.984. The results indicate that EL methods can enhance individual machine learning methods significantly.

Suggested Citation

  • Kesriklioğlu, Esma & Oktay, Erkan & Karaaslan, Abdulkerim, 2023. "Predicting total household energy expenditures using ensemble learning methods," Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223009751
    DOI: 10.1016/j.energy.2023.127581
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    as
    1. Fateh Belaid & Christophe Rault, 2020. "Energy Expenditure in Egypt: Empirical Evidence Based on A Quantile Regression Approach," Working Papers 1446, Economic Research Forum, revised 20 Dec 2020.
    2. Narasimha Rao, M. & Reddy, B. Sudhakara, 2007. "Variations in energy use by Indian households: An analysis of micro level data," Energy, Elsevier, vol. 32(2), pages 143-153.
    3. Meyar-Naimi, H. & Vaez-Zadeh, S., 2012. "Sustainable development based energy policy making frameworks, a critical review," Energy Policy, Elsevier, vol. 43(C), pages 351-361.
    4. Hadjout, D. & Torres, J.F. & Troncoso, A. & Sebaa, A. & Martínez-Álvarez, F., 2022. "Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market," Energy, Elsevier, vol. 243(C).
    5. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    6. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
    7. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    8. Stephenson, Janet & Barton, Barry & Carrington, Gerry & Gnoth, Daniel & Lawson, Rob & Thorsnes, Paul, 2010. "Energy cultures: A framework for understanding energy behaviours," Energy Policy, Elsevier, vol. 38(10), pages 6120-6129, October.
    9. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
    10. Valenzuela, Carlos & Valencia, Alelhie & White, Steve & Jordan, Jeffrey A. & Cano, Stephanie & Keating, Jerome & Nagorski, John & Potter, Lloyd B., 2014. "An analysis of monthly household energy consumption among single-family residences in Texas, 2010," Energy Policy, Elsevier, vol. 69(C), pages 263-272.
    11. Feng, Yanxiao & Duan, Qiuhua & Chen, Xi & Yakkali, Sai Santosh & Wang, Julian, 2021. "Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods," Applied Energy, Elsevier, vol. 291(C).
    12. Rahut, Dil Bahadur & Behera, Bhagirath & Ali, Akhter, 2016. "Household energy choice and consumption intensity: Empirical evidence from Bhutan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 993-1009.
    13. Chen, Kunlong & Jiang, Jiuchun & Zheng, Fangdan & Chen, Kunjin, 2018. "A novel data-driven approach for residential electricity consumption prediction based on ensemble learning," Energy, Elsevier, vol. 150(C), pages 49-60.
    14. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    15. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    16. Jurado, Sergio & Nebot, Àngela & Mugica, Fransisco & Avellana, Narcís, 2015. "Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques," Energy, Elsevier, vol. 86(C), pages 276-291.
    17. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    18. Vringer, Kees & Aalbers, Theo & Blok, Kornelis, 2007. "Household energy requirement and value patterns," Energy Policy, Elsevier, vol. 35(1), pages 553-566, January.
    19. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    20. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, June.
    21. Brounen, Dirk & Kok, Nils & Quigley, John M., 2012. "Residential energy use and conservation: Economics and demographics," European Economic Review, Elsevier, vol. 56(5), pages 931-945.
    22. Rahut, Dil Bahadur & Das, Sukanya & De Groote, Hugo & Behera, Bhagirath, 2014. "Determinants of household energy use in Bhutan," Energy, Elsevier, vol. 69(C), pages 661-672.
    23. Shi, Jiaqi & Li, Chenxi & Yan, Xiaohe, 2023. "Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization," Energy, Elsevier, vol. 262(PB).
    24. Adusah-Poku, Frank & Takeuchi, Kenji, 2019. "Household energy expenditure in Ghana: A double-hurdle model approach," World Development, Elsevier, vol. 117(C), pages 266-277.
    25. Pallegedara, Asankha & Mottaleb, Khondoker Abdul & Rahut, Dil Bahadur, 2021. "Exploring choice and expenditure on energy for domestic works by the Sri Lankan households: Implications for policy," Energy, Elsevier, vol. 222(C).
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