Combining an improved Apriori algorithm and Social Network analysis to identify the unique sequential features of individual household electricity consumption behaviours
| Author(s): |
Li Li
(School of Economics and Management, Beijing Information Science and Technology University, Beijing, People’s Republic of China)
Shiyu Deng (School of Computer Science, Beijing Information Science and Technology University, Beijing, People’s Republic of China) Yichen Xiong (School of Computer Science, Beijing Information Science and Technology University, Beijing, People’s Republic of China) Jianjun Wang (School of Economics and Management, North China Electric Power University, Beijing, People’s Republic of China) Hua Cai (School of Industrial Engineering, Purdue University, West Lafayette, IN, USA) Jian Zhang (School of Economics and Management, Beijing Information Science and Technology University, Beijing, People’s Republic of China) Songliang Guo (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, People’s Republic of China) Jing Zhang (China Institute of Boundary and Ocean Studies, Wuhan University, Wuhan, People’s Republic of China) Fang Liu (School of Economics and Management, Beijing Information Science and Technology University, Beijing, People’s Republic of China) Tianfeng Li (School of Economics and Management, Beijing Information Science and Technology University, Beijing, People’s Republic of China) |
|---|---|
| Medium: | journal article |
| Language(s): | English |
| Published in: | Advances in Building Energy Research, 26 June 2024, n. 4, v. 18 |
| Page(s): | 1-31 |
| DOI: | 10.1080/17512549.2024.2361361 |
- About this
data sheet - Reference-ID
10788996 - Published on:
20/06/2024 - Last updated on:
31/08/2024
Structurae cooperates with
