Department of Power, Electronic and Telecommunication Engineering Faculty of Technical Sciences, University of Novi Sad, Serbia
February 27, 2017, 11:00, Room GC C2 413 (click here for the map)
An important part of any power distribution management system data model is a model of load type. A load type represents typical load behaviour of a group of similar consumers, e.g. a group of residential, industrial or commercial consumers. A common method for creation of load types is the clustering of individual energy consumers based on their yearly consumption behaviour. To reach the satisfactory level of load type quality, the crucial decision is a choice of proper clustering similarity measure. In this talk, a comparison of different metrics, used as similarity measures in our process of load type creation, will be presented. Additionally, a novel metric, also included in the comparison, will be introduced. The metrics and the quality of load types created therewith are assessed by using a real data set obtained from the distribution network smart meters.
Nikola Obrenovic is an assistant professor at the Department of Power, Electronic and Telecommunication Engineering, Faculty of Technical Sciences, University of Novi Sad, and teaches BSc courses about databases and database design. He received his PhD degree in Electrical and Computer Engineering - Computing and Control Engineering from the University of Novi Sad, in October 2015. His doctoral studies and thesis incorporated areas of model-driven database development, rapid prototyping and automatic design verification. Currently, his research interests include databases, database design and data science, with applications to smart grid management systems. Additionally, Nikola Obrenovic is employed as a software architect at Schneider Electric DMS NS, Novi Sad, Serbia. His primary duties are design and development of various software solutions, which are part of Schneider Electric's Advanced Distribution Management System. The developed solutions belong to the fields of data mining, data warehouses, business intelligence and historian databases.