| Title |
Load Pattern Clustering Approach for P2P Electricity Transaction |
| Authors |
오예준(Ye Jun Oh) ; 박용국(Yong Kuk Park) ; 이민구(Min Goo Lee) ; 김태원(Tae Won Kim) |
| DOI |
https://doi.org/10.5573/ieie.2020.57.11.72 |
| Keywords |
Load pattern clustering; P2P electricity transaction; Window size; Time slot size |
| Abstract |
In this paper, we propose a clustering approach to classify the electricity load pattern of each customer of apartments and commercial buildings. Currently, a variety of P2P(peer to peer) electricity transaction model have been implementing domestically and abroad, and the estimation of monthly, daily, and hourly electricity load pattern through classification of each customer’s load pattern is crucial for an effective electricity transaction. Therefore, the window size and time slot size which is a basic time unit for clustering and maximize a clustering quality, respectively were derived. Consequently, the optimization performance of the proposed approach on a clustering quality can be identified by means of applying seasonal load data of apartments and commercial buildings. |