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

Nitin Kumar Singh, Takuya Fukushima and Masaaki Nagahara
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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

Energies, 2023, vol. 16, issue 16, 1-10

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.

Keywords: gradient boosting; LightGBM; k -means clustering; time-series data; questionnaire survey; home energy management systems; zero energy houses (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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