YYÜ GCRIS Basic veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.

Browsing by Author "Yesilbudak, Mehmet"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Conference Object
    Multi-Period Prediction of Solar Radiation Using Arma and Arima Models
    (Elsevier Science Bv, 2015) Colak, Ilhami; Yesilbudak, Mehmet; Genc, Naci; Bayindir, Ramazan
    Due to the variations in weather conditions, solar power integration to the electricity grid at a high penetration rate can cause a threat for the grid stability. Therefore, it is required to predict the solar radiation parameter in order to ensure the quality and the security of the grid. In this study, initially, a 1-h time series model belong to the solar radiation parameter is created for multi-period predictions. Afterwards, autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are compared in terms of the goodness-of-fit value produced by the log-likelihood function. As a result of determining the best statistical models in multi-period predictions, one-period, two-period and three-period ahead predictions are carried out for the solar radiation parameter in a comprehensive way. Many feasible comparisons have been made for the solar radiation prediction.
  • Thumbnail Image
    Conference Object
    A Novel Application of Naive Bayes Classifier in Photovoltaic Energy Prediction
    (Ieee, 2017) Bayindir, Ramazan; Yesilbudak, Mehmet; Colak, Medine; Genc, Naci
    Solar energy is one of the most affordable and clean renewable energy source in the world. Hence, the solar energy prediction is an inevitable requirement in order to get the maximum solar energy during the day time and to increase the efficiency of solar energy systems. For this purpose, this paper predicts the daily total energy generation of an installed photovoltaic system using the Naive Bayes classifier. In the prediction process, one-year historical dataset including daily average temperature, daily total sunshine duration, daily total global solar radiation and daily total photovoltaic energy generation parameters are used as the categorical-valued attributes. By means of the Naive Bayes application, the sensitivity and the accuracy measures are improved for the photovoltaic energy prediction and the effects of other solar attributes on the photovoltaic energy generation are evaluated.