Clustering Method Comparison for Rural Occupant’s Behavior Based on Building Time-Series Energy Data

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident’s time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that KmEuclidean and Kmₛhape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67. 05% and 65. 09%. KmDTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35. 29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision.