| Title |
Typical Daily Load Profile Generation using Load Profile of Automatic Meter Reading Customer |
| Authors |
김영일(Kim, Young-Il) ; 신진호(Shin, Jin-Ho) ; 이봉재(Yi, Bong-Jae) ; 양일권(Yang, Il-Kwon) |
| Keywords |
Typical Load Profile ; Clustering ; Automatic Meter Reading ; AMR |
| Abstract |
Recently, distribution load analysis using AMR (Automatic Meter Reading) data is researched in electric utilities. Load analysis method based on AMR system generates the typical load profile using load data of AMR customers, estimates the load profile of non-AMR customers, and analyzes the peak load and load profile of the distribution circuits and sectors per every 15 minutes/hour/day/week/month. Typical load profile is generated by the algorithm calculating the average amount of power consumption of each groups having similar load patterns. Traditional customer clustering mechanism uses only contract type code as a key. This mechanism has low accuracy because many customers having same contract code have different load patterns. In this research, We propose a customer clustring mechanism using k-means algorithm with contract type code and AMR data. |