| Title |
An Analysis of Energy Consumption Types Considering Life Patterns of Single-person Households |
| Authors |
Lee, Seunghui ; Jung, Sungwon ; Lim, Ki-Taek |
| DOI |
https://doi.org/10.5659/JAIK_PD.2019.35.1.37 |
| Keywords |
Building energy efficiency rating ; Single person household ; Energy Plus ; Machine Learning ; User Clustering |
| Abstract |
The energy of the building is influenced by the user 's activity due to the population, society, and economic characteristics of the building user. In order to obtain accurate energy information, the difference in the amount of energy consumption by the activities and characteristics of building users should be identified. The purpose of the study is to identify the difference in the amount of energy consumption by the user's activities in the same building, and to analyse the relationship between user's activities and demographic, social and economic characteristics. For research, energy simulation is performed based on actual user activity schedule. The results of the simulation were clustered by using K-Means clustering, a machine learning technique. As a result, four types of users were derived based on the amount of energy consumption. The more energy used in a cluster, the lower the user's income level and older. The longer a user's indoor activity times, the higher the energy use, and these activities relate to the user's characteristics. There is more than twice the difference between the group that uses the least energy consumption and the group that uses the most energy consumption. |