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Gradient Boosting Approach to Predict Energy-Saving Awareness of Households in Kitakyushu

Author

Listed:
  • Nitin Kumar Singh

    (Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan)

  • Takuya Fukushima

    (Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma 630-0192, Japan)

  • Masaaki Nagahara

    (Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan)

Abstract

This paper aims to develop a machine-learning model based on a gradient-boosting algorithm to predict the energy-saving awareness of households using a questionnaire survey and 11-month energy data collected from more than 200 smart houses in Kitakyushu, Japan. We utilize the LightGBM (light gradient boosting machine) classifier to perform feature selection for the prediction. By using this approach, we demonstrate that the key features are the standard deviations of electricity purchased between 8 a.m. and 9 a.m. and electricity consumed between 7 p.m. and 9 p.m. Next, by using k -means clustering we split the households based on the obtained features into three groups. Finally, by using statistical hypothesis testing, we prove that these three groups have statistically distinct levels of energy-saving awareness. This model enables us to detect eco-friendly households from their energy data, which may support energy policymaking.

Suggested Citation

  • Nitin Kumar Singh & Takuya Fukushima & Masaaki Nagahara, 2023. "Gradient Boosting Approach to Predict Energy-Saving Awareness of Households in Kitakyushu," Energies, MDPI, vol. 16(16), pages 1-10, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5998-:d:1218187
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    References listed on IDEAS

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