Browsing by Author "Gülez, Kayhan"

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    Performance Evaluations of Next Generation Networks for Smart Grid Applications
    (Institute of Electrical and Electronics Engineers Inc., 2015) Tuna, Gürkan; Ayana, Esra Kaya; Gülez, Kayhan; Kiokes, George C.; Güngör, Vehbi Çağrı
    Smart Grid (SG) can be described as the concept of modernizing the traditional electrical grid. Through the addition of SG technologies traditional electrical grids become more flexible, robust and interactive, and are able to provide real time feedback by employing innovative services and products together with communication, control, intelligent monitoring, and self-healing technologies. For being fully functional, utility operators deploy various SG applications to handle the key requirements including delivery optimization, demand optimization and asset optimization needs. The SG applications can be categorized into two main classes: grid-focused applications and customer-focused applications. Although these applications differ in terms of security, Quality of Service (QoS) and reliability, their common requirement is a communication infrastructure. In this paper, we focus on the use of Next Generation Networks (NGNs) for SG applications. We also present a detailed analysis of a NGN-based communication infrastructure for SG applications in terms of global network statistics and node-level statistics. © 2018 Elsevier B.V., All rights reserved.
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    Citation - WoS: 7
    Citation - Scopus: 10
    PI-Controlled ANN-Based Energy Consumption Forecasting for Smart Grids
    (SciTePress, 2015) Gezer, Gülsüm; Tuna, Gürkan; Κogias, DImitrios G.; Gülez, Kayhan; Güngör, Vehbi Çağrı
    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. © 2022 Elsevier B.V., All rights reserved.