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Article Citation - WoS: 17Citation - Scopus: 18Daily Pm10, Periodicity and Harmonic Regression Model: the Case of London(Pergamon-elsevier Science Ltd, 2020) Okkaoglu, Yasin; Akdi, Yilmaz; Unlu, Kamil DemirberkOne of the most important and distinguishable features of the climate driven data can be shown as the seasonality. Due to its nature air pollution data may have hourly, daily, weekly, monthly or even seasonal cycles. Many techniques such as non-linear time series analysis, machine learning algorithms and deterministic models, have been used to deal with this non-linear structure. Although, these models can capture the seasonality they can't identify the periodicity. Periodicity is beyond the seasonality, it is the hidden pattern of the time series. In this study, it is aimed to investigate the periodicity of daily Particulate Matter (PM10) of London between the periods 2014 and 2018. PM10 is the particulate matter of which aerodynamic diameter is less than 10 mu m. Firstly, periodogram based unit root test is used to check the stationarity of the investigated data. Afterwards, hidden periodic structure of the data is revealed. It is found that, it has five different cycle periods as 7 days, 25 days, 6 months, a year and 15 months. Lastly, it is shown that harmonic regression performs better in forecasting monthly and daily averages of the data.Article Citation - WoS: 20Citation - Scopus: 23A New Generalized Δ-Shock Model and Its Application To 1-out-of-(m+1):g Cold Standby System(Elsevier Sci Ltd, 2023) Eryilmaz, Serkan; Unlu, Kamil DemirberkAccording to the classical delta-shock model, the system failure occurs upon the occurrence of a new shock that arrives in a time length less than delta, a given positive value. In this paper, a new generalized version of the delta-shock model is introduced. Under the proposed model, the system fails if there are m shocks that arrive in a time length less than delta after a previous shock, m >= 1. The mean time to failure of the system is approximated for both discretely and continuously distributed intershock time distributions. The usefulness of the model is also shown to study 1-out-of-(m + 1):G cold standby system. Illustrative numerical results are presented for geometric, exponential, discrete and continuous phase-type intershock time distributions.Article Citation - WoS: 23Citation - Scopus: 26A Univariate Time Series Methodology Based on Sequence-To Learning for Short To Midterm Wind Power Production(Pergamon-elsevier Science Ltd, 2022) Akbal, Yildirim; Unlu, Kamil DemirberkThe biggest wind farm of Turkey is placed at Manisa which is located in the Aegean Region. Electricity is a nonstorable commodity for that reason, it is very important to have a strong forecast and model of the potential electricity production to plan the electricity loads. In this study, the aim is to model and forecast electricity production of the wind farms located at Manisa by using a univariate model based on sequence-to-sequence learning. The forecasting range of the study is from short term to midterm. The strength of the proposed model is that; it only needs its own lagged value to make forecasts. The empirical evidences show that the model has high coefficient of variation (R-2) in short term and moderate R-2 in the midterm forecast. Although in the midrange forecasts R-2 slightly decreases mean squared error and mean absolute error shows that the model is accurate also in the midterm forecasts. The proposed model is not only strong in hourly electricity production forecasts but with a slight modification also in forecasting the minimum, maximum and average electricity production for a fixed range. This study concludes with two fresh and intriguing future research ideas.
