شماره ركورد كنفرانس :
3788
عنوان مقاله :
AMI Data Analytics; An Investigation of the SelfOrganizing Maps Capabilities in Customers Characterization and Big Data Management
عنوان به زبان ديگر :
AMI Data Analytics; An Investigation of the SelfOrganizing Maps Capabilities in Customers Characterization and Big Data Management
پديدآورندگان :
Kojury-Naftchali Mohsen kojury.savadkoohnaft@gmail.com SMRL, CIPCE, ECE,University of Tehran, Tehran, Iran , Fereidunian Alireza fereidunian@ee.kntu.ac.ir CReaTech, EE, K.N.Toosi University of Technology, Tehran, Iran , Lesani Hamid lesani@ut.ac.ir SMRL, CIPCE, School of ECE,University of Tehran, Tehran, Iran
كليدواژه :
— advanced metering infrastructure (AMI) data , self , Organizing Map (SOM) , Data mining , big data , and consumption patterns.
عنوان كنفرانس :
هفتمين كنفرانس ملي شبكه هاي هوشمند انرژي 96
چكيده فارسي :
This paper is aimed at investigating self-organizing
map (SOM) capabilities in customers characterization in their
electricity consumption behavior. Characterization is based on
the recorded data by Advanced Metering Infrastructure (AMI)
in smart grid. This investigation regards two aspects of SOM:
First, capabilities of SOM in pattern recognition applications and
second, capabilities of SOM in big data management.
Both of these capabilities are instrumental in the current
restructured electricity market. From one aspect, requirements
of the market for load profiling by which decision making in
energy management programs and other policies is more
reliable. From another aspect, the increase in information
exchanging in the grid in the presence of AMI which complicates
the analysis of data.
Applying this algorithm in both two aforementioned aspects has
shown persuasive results. A real dataset related to Irish
electricity consumption is used to evaluate performance of the
proposed procedures.
چكيده لاتين :
This paper is aimed at investigating self-organizing
map (SOM) capabilities in customers characterization in their
electricity consumption behavior. Characterization is based on
the recorded data by Advanced Metering Infrastructure (AMI)
in smart grid. This investigation regards two aspects of SOM:
First, capabilities of SOM in pattern recognition applications and
second, capabilities of SOM in big data management.
Both of these capabilities are instrumental in the current
restructured electricity market. From one aspect, requirements
of the market for load profiling by which decision making in
energy management programs and other policies is more
reliable. From another aspect, the increase in information
exchanging in the grid in the presence of AMI which complicates
the analysis of data.
Applying this algorithm in both two aforementioned aspects has
shown persuasive results. A real dataset related to Irish
electricity consumption is used to evaluate performance of the
proposed procedures.
