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Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Now showing 1 - 4 of 4
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 8
    Short Term Electricity Load Forecasting: A Case Study of Electric Utility Market in Turkey
    (Institute of Electrical and Electronics Engineers Inc., 2015-04) Ishik, Muhammed Yasin; Göze, Tolga; Ozcan, Ihsan; Güngör, Vehbi Çağrı; Aydin, Zafer; Yasin, Muhammed
    With the recent developments in energy sector, the pricing of electricity is now governed by the spot market where a variety of market mechanisms are effective. After the new legislation of market liberalization in Turkey, competition-based on hourly price has received a growing interest in the energy market, which necessitated generators and electric utility companies to add new dimensions to their scope of operation: short-term load and price forecasting. The field has several opportunities though not free from challenges. The dynamic behavior of the market price has caused the electric load to become variable and non-stationary. Furthermore, the number of nodes, in which the load must be predicted, is not constant anymore and can no longer be estimated by experts alone. In this competitive scenario, statistical forecasting methods that can automatically and accurately process thousands of data samples are essential. The purpose of this study is to demonstrate the importance of short-term load forecasting, how it has received a growing interest in Turkey and to propose an artificial neural network that can forecast the short term electricity load. Through detailed performance evaluations, we demonstrate that our forecasting method is capable of predicting the hourly load accurately. © 2017 Elsevier B.V., All rights reserved.
  • Conference Object
    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; Kogias, Dimitris
    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.
  • Conference Object
    Citation - Scopus: 5
    Development of Knowledge Based Response Correction for a Reconfigurable N-Shaped Microstrip Antenna Design
    (Institute of Electrical and Electronics Engineers Inc., 2015-08) Aoad, Ashrf; Simsek, Murat; Aydin, Zafer
    This study presents the use of prior knowledge of inverse artificial neural network (ANN) to model and optimize a reconfigurable N-shaped microstrip antenna. Three accurate prior knowledge inverse ANNs with large amount training data are proposed where the frequency information is incorporated into the structure of ANN. The complexity of the input/output relationship is reduced by using prior knowledge. Three separate methods of incorporating knowledge in the second step of the training process with a multilayer perceptron (MLP) in the first step are demonstrated and their results are compared to EM simulation. © 2023 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Developing New Empirical Formulae for the Resilient Modulus of Fine-Grained Subgrade Soils Using a Large Long-Term Pavement Performance Dataset and Artificial Neural Network Approach
    (Sage Publications inc, 2021-12-17) Fedakar, Halil Ibrahim
    Artificial neural network (ANN) has been successfully used for developing prediction models for resilient modulus (M-r). However, no reliable M-r formula derived from these models has been proposed in previous studies, although engineers/researchers need empirical formulae for hand calculation of M-r. Therefore, this study aimed to propose reliable empirical formulae for the M-r of fine-grained soils using ANN. For this purpose, thousands of ANN models were developed using the long-term pavement performance (LTPP) and external datasets. The input parameters were the percentage of soil particles passing through #200 sieve (P200), silt percentage (SP), clay percentage (CP), liquid limit (LL), plasticity index (PI), maximum dry density ([rho(dry)](max)), optimum moisture content (w(opt)), confining pressure (sigma(c)), and nominal maximum axial stress (sigma(z)). The ANN models were compared with several constitutive models. The results indicate that the constitutive models failed to predict the M-r, and the best M-r predictions were obtained by the ANN-C9 (P200, SP, CP, LL, PI, sigma(c), and sigma(z)), ANN-C10 (P200, SP, CP, [rho(dry)](max), w(opt), sigma(c), and sigma z), and ANN-C11 (P200, SP, CP, LL, PI, [rho(dry)](max), w(opt), sigma(c), and sigma(z)) models. Thus, the structures of these ANN models were formulated and proposed as the new empirical formulae for the M-r of fine-grained soils. Sensitivity analysis was also performed on these ANN models. It was determined that (rho(dry))(max) is the most influential parameter in the ANN-C10 model, and LL is the most influential parameter in the ANN-C9 and ANN-C11 models. On the other hand, sigma(c) and sigma(z) are the least influential parameters.