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

Author

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  • 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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    1. Dorian, James P. & Franssen, Herman T. & Simbeck, Dale R., 2006. "Global challenges in energy," Energy Policy, Elsevier, vol. 34(15), pages 1984-1991, October.
    2. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    3. Pengfei Sheng & Yaping He & Xiaohui Guo, 2017. "The impact of urbanization on energy consumption and efficiency," Energy & Environment, , vol. 28(7), pages 673-686, November.
    4. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    5. Estiri, Hossein, 2014. "Building and household X-factors and energy consumption at the residential sector," Energy Economics, Elsevier, vol. 43(C), pages 178-184.
    6. Dong, Kangyin & Hochman, Gal & Timilsina, Govinda R., 2020. "Do drivers of CO2 emission growth alter overtime and by the stage of economic development?," Energy Policy, Elsevier, vol. 140(C).
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    Cited by:

    1. Nitin Kumar Singh & Masaaki Nagahara, 2024. "LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses," Energies, MDPI, vol. 17(17), pages 1-32, September.
    2. Nibedita Mahanta & Ruma Talukdar, 2024. "Forecasting of Electricity Consumption by Seasonal Autoregressive Integrated Moving Average Model in Assam, India," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 393-400, September.

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