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

• Published in: Energies (Basel) Vol. 16; no. 16; p. 5998
• Main Authors: Singh, Nitin Kumar, Fukushima, Takuya, Nagahara, Masaaki
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Algorithms; Alternative energy; Behavior modification; Data analysis; Datasets; Decision trees; Dwellings; Electricity; Emissions; Energy conservation; Energy consumption; Energy industry; Energy management; Energy management systems; Energy use; Fossil fuels; gradient boosting; home energy management systems; Households; Housing; Hypothesis testing; Japan; k-means clustering; LightGBM; Machine learning; Marketing research; questionnaire survey; Questionnaires; Smart houses; time-series data; Japan
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16165998

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.