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Article Citation - WoS: 8Citation - Scopus: 10Computing Reliability Indices of a Wind Power System Via Markov Chain Modelling of Wind Speed(Sage Publications Ltd, 2024) Eryilmaz, Serkan; Bulanik, Irem; Devrim, YilserStatistical modelling of wind speed is of great importance in the evaluation of wind farm performance and power production. Various models have been proposed in the literature depending on the corresponding time scale. For hourly observed wind speed data, the dependence among successive wind speed values is inevitable. Such a dependence has been well modelled by Markov chains. In this paper, the use of Markov chains for modelling wind speed data is discussed in the context of the previously proposed likelihood ratio test. The main steps for Markov chain based modelling methodology of wind speed are presented and the limiting distribution of the Markov chain is utilized to compute wind speed probabilities. The computational formulas for reliability indices of a wind farm consisting of a specified number of wind turbines are presented through the limiting distribution of a Markov chain. A case study that is based on real data set is also presented.Article Citation - WoS: 13Citation - Scopus: 16An Analytic Network Process Based Risk Assessment Model for Ppp Hydropower Investments(Vilnius Gediminas Tech Univ, 2021) Akcay, Emre CanerThe number of public-private partnership (PPP) projects has gone up especially in developing countries. The risk assessment of PPP projects is essential in ensuring project success. The objective of this study is to develop an Analytic Network Process (ANP) based risk assessment model for hydropower investments, and a tool to facilitate quantification of risk ratings based on this model. The results show that the three most important risk factors that affect the overall risk rating of a PPP hydropower investment are legal risks, contractor/subcontractor risks, and operator risks. In addition, the three most important risk clusters were identified as stakeholders, government requirements, and resources, whereas market was the least important cluster. The tool that measures the risk rating of a PPP of hydropower project was tested on ten real cases, and satisfactory results were obtained in terms of its predictive capability. The contributions of this research include (1) identification of the risk factors and clusters of factors associated with PPP hydropower investments; (2) determination of the priority of each risk factor and cluster; (3) development a tool that guides the investors through the risk assessment of PPP hydropower investments.Article Citation - WoS: 9Citation - Scopus: 9A Hybrid Deep Learning Methodology for Wind Power Forecasting Based on Attention(Taylor & Francis inc, 2024) Akbal, Yildirim; Unlu, Kamil DemirberkWind energy, as a sustainable energy source, poses challenges in terms of storage. Therefore, careful planning is crucial to utilize it efficiently. Deep learning algorithms are gaining popularity for analyzing complex time series data. However, as the "no free lunch" theorem suggests, the trade-off is: they need a lot of data to achieve the benefits. This even brings up a severe challenge for time series analysis, as the availability of historical data is often limited. This study aims to address this issue by proposing a novel shallow deep learning approach for wind power forecasting. The proposed model utilizes a fusion of transformers, convolutional and recurrent neural networks to efficiently handle several time series simultaneously. The empirical evidence demonstrates that the suggested innovative method exhibits exceptional forecasting performance, as indicated by a coefficient of determination (R2) of 0.99. When the forecasting horizon reaches 48, the model's performance declines significantly. However, when dealing with long ranges, utilizing the mean as a metric rather than individual point estimates would yield superior results. Even when forecasting up to 96 hrs in advance, obtaining an R2 value of 0.50 is considered a noteworthy accomplishment in the context of average forecasting.
