Authors:
Gulsum Gezer
1
;
Gurkan Tuna
2
;
Dimitris Kogias
3
;
Kayhan Gulez
1
and
V. Cagri Gungor
4
Affiliations:
1
Yildiz Technical University, Turkey
;
2
Trakya University, Turkey
;
3
Piraeus University of Applied Sciences, Greece
;
4
Abdullah Gul University, Turkey
Keyword(s):
Smart Grid, Demand Forecasting, Artificial Neural Network, Optimization.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Neural Networks Based Control Systems
Abstract:
Although Smart Grid (SG) transformation brings many advantages to electric utilities, the longstanding
challenge for all them is to supply electricity at the lowest cost. In addition, currently, the electric utilities
must comply with new expectations for their operations, and address new challenges such as energy
efficiency regulations and guidelines, possibility of economic recessions, volatility of fuel prices, new user
profiles and demands of regulators. In order to meet all these emerging economic and regulatory realities,
the electric utilities operating SGs must be able to determine and meet load, implement new technologies
that can effect energy sales and interact with their customers for their purchases of electricity. In this
respect, load forecasting which has traditionally been done mostly at city or country level can address such
issues vital to the electric utilities. In this paper, an artificial neural network based energy consumption
forecasting system is proposed and
the efficiency of the proposed system is shown with the results of a set
of simulation studies. The proposed system can provide valuable inputs to smart grid applications.
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