Takuya Fukushima
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Takuya Fukushima has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Masaaki Nagahara
Hiroshima University
1 shared publication - Nitin Kumar Singh
Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology 2-4 Hibikino Wakamatsu 808-0196 Kitakyushu Japan
1 shared publication
Latest Publications
- Open Access
Gradient Boosting Approach to Predict Energy-Saving Awareness of Households in Kitakyushu
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.
Open AccessGradient Boosting Approach to Predict Energy-Saving Awareness of Households in Kitakyushu
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.