Ahmet Öztopal

On the regional wind energy potential of Turkey

Continuous uses of fossil fuels are bound to pollute the atmosphere and consequently unwanted greenhouse and climate change effects will come to dominate every part of the earth. It is, therefore, advised to exploit clean energy resources, and for many nations in the world to try to assess their environmentally friendly, clean energy resources such as wind energy. Hence, it is an urgent situation to determine the wind energy potential in every country. This paper gives the wind velocity, topography and wind energy variation maps obtained for Turkey with local and regional interpretations.

Genetic algorithms for the classification and prediction of precipitation occurrence

Abstract Using an approach similar to the biological processes of natural selection and evolution, the genetic algorithm (GA) is a nonconventional optimum search technique. Genetic algorithms have the ability to search large and complex decision spaces and handle nonconvexities. In this paper, the GA is applied for solving the optimum classification of rainy and non-rainy day occurrences based on vertical velocity, dewpoint depression, temperature and humidity data. The problem involves finding optimum classification based on known data, training the future prediction system and then making reliable predictions for rainfall occurrences which have significance in agricultural, transportation, water resources and tourism activities. Various statistical approaches require restrictive assumptions such as stationarity, homogeneity and normal probability distribution of the hydrological variables concerned. The GAs do not require any of these assumptions in their applications. The GA approach for the occurrence classifications and predictions is presented in steps and then the application of the methodology is shown for precipitation occurrence (non-occurrence) data. It has been shown that GAs give better results than classical approaches such as discriminant analysis. The application of the methodology is presented independently for the precipitation event occurrences and forecasting at the Lake Van station in eastern Turkey. Finally, the amounts of precipitation are predicted with a model similar to a third order Markov model whose parameters are estimated by the GA technique.

Application of genetic algorithm for determination of Angström equation coefficients

Abstract Genetic algorithm analysis of the monthly average daily irradiation and sunshine duration data of four Turkish locations has been performed for determining the Angstrom equation coefficients. This analysis provides the parameter estimations as new data are recorded and consequently yields the time variability of the parameters. Application of the genetic algorithm procedure is presented in this paper for determining the coefficients and their statistical distributions as well as the relationships between them. Good correlation is obtained ( r ≥0.93) in all the cases, showing the validity of the Angstrom equation for Turkish locations. Besides, the analysis presents a new way of estimating the Angstrom equation parameters.

Solar Irradiation Estimation from Sunshine Duration by Geno-Fuzzy Partial Approach

Terrestrial solar irradiation estimations are obtained mostly from the sunshine duration measurements through almost linear models such as the classical Angstrom formulation. The parameters of such model are estimated through the least squares technique in practical studies. In this article, more efficient model based on the fuzzy system concept is proposed for the architecture of solar irradiation estimation from the sunshine duration measurements. Especially, partial fuzzy modeling accounts for the possible local nonlinearities in the form of piece-wise linearizations. The parameters estimation of such a fuzzy model is achieved through the application of genetic algorithm technique. The fuzzy part of the model provides treatment of vague information about the sunshine duration data whereas the genetic part furnishes an objective and optimum estimation procedure. The application of geno-fuzzy model as proposed in this article is presented for three stations in Turkey and the results are constrasted by the previous classical approaches.

Innovative Trend Methodology Applications to Precipitation Records in Turkey

There are trend identification methodologies in the literature but they have three crucial points that should be cared with great attention in practical applications. These points are that the available time series should have independent serial correlation structure, Gaussian (normal) distribution and monotonic trend fitting to whole time series through the least square technique. The existing methods consider the whole duration of the time series and try to identify a monotonic trend in increasing or decreasing forms. This paper presents partial trend methodology as an innovative and simple trend identification method, which yields low, medium and high cluster trends separately. It provides the opportunity to segregate between the low, high and medium flow trends and their relative intensities, durations as well as magnitudes. The application of the partial trend methodology is presented for 7 precipitation stations from 7 different sub-climatic regions of Turkey leading to spatial and temporal trend interpretations.

Determination of the Representative Time Horizons for Short-term Wind Power Prediction by Using Artificial Neural Networks

Wind power is one of the major renewable energy sources, and this source has reached to compete with conventional energies. Wind speed has very complex variations during different time horizons. Prediction of wind speed shows some uncertainties depending on atmospheric parameters, such as temperature, pressure, solar irradiation, and relative humidity. Additionally, wind turbines could not generate electricity at all wind speed values that are less than cut-in and greater than cut-out. These conditions add new uncertainties to the prediction of this meteorological parameter. In this case, wind power prediction from generated electricity data will be better than direct wind speed or other meteorological parameters. Generally, in engineering applications wind power prediction is based on hourly mean, in other words, one hour time horizon data. This time horizon is accepted as a reference and representative but physically this horizon does not represent data homogeneity and should be changed depending on location and time of year. The main aim of this article is to determine the highest representative time horizon to predict wind power in a considered system. First, artificial neural networks methodology is applied to generated power with a 1.5-kW wind turbine by using 1 and 60 minutes average data. It is seen that 35 minutes time horizon gives the best representative scale for wind power prediction in April and 15 minutes in August, respectively. During these months, errors between measured and testing values are decreased to 9.72 and 3.62%, respectively, for the mentioned time horizons. Especially, it is seen that mean values that are evaluated from less than 8–10 minutes and greater than 40 minutes time horizon data give high errors. In other words, using very short time horizons of power data cause high prediction variations and, as a result of these deviations, wind power prediction shows high errors.

Extreme precipitation climate change scenario evaluation over Turkey

Extreme precipitation events are the most important quantities for flood occurrences in any area and especially for groundwater recharge in arid and semi-arid regions. Their future predictions help to provide a scientific basis for evaluation and management of water resources potential. The main purpose of this paper is to expose the extreme precipitation occurrence estimations based on the A1B scenario for Turkey by using a regional climate model (RCM). According to first result of this study, although the winter season results indicate significant increase in the extreme precipitation amount around the Northern Aegean and Eastern Black Sea regions for near future (2021-2060) and around Southern Aegean and Western Black Sea regions for far future periods (2061-2100), significantly decreasing trend appear in the Northern Iraq, Northern Syria, Mediterranean coasts and Southeastern Anatolia for both future periods. Another important result is that increase in the precipitation is expected in the Northern Iraq, Northern Syria and Southeastern Anatolia for both future periods in autumn season.

Terrestrial irradiation-sunshine duration clustering and prediction

Abstract The relationship between terrestrial irradiance and sunshine duration is not crisp and there are scatters around a general trend, which most often is expressed to occur in the form of a linear expression. This study presents a way of grouping the solar irradiation–sunshine duration data into convenient seasonal subgroups and then makes predictions within each of the groups quantitatively. In the classical Angstrom or other approaches, the seasonal variations are not considered, and therefore, rather global parameter estimations are obtained. However, the seasonal methodology of this paper provides more detailed interpretations in addition to seasonal effects and parameter estimations.

Climate Change Expectations in the Next Half Century of Turkey

Turkey as one of the mid-latitude countries in sub-tropical climate belt of the world has a special location meteoro-hydrologically. Various air mass movements display their impacts over Turkey’s air, weather, and climate situations on a daily, monthly, or yearly base. Such a meteorologically interactive region is expected to have climate change signature variations as a result of various air movements in the future. A set of values from over three decades of observed temperatures, records from two overlapping periods 1960–1990 and 1970–2000 based on the records from the 30 years in the past, are taken. The basic time duration between 1960 and 1990 displays the actions as observed in the regular time period in the world. This time is considered as a base to determine the state of future temp changes. This study avoids statistical comparisons based on arithmetic averages and standard deviations; instead it adopts a method of interpreting the changes over the last three decades based on Special Report on Emissions Scenarios for different time intervals (5-, 10-, and 50-year). In this work, after establishing a special downscaling methodology, different General Climate Model results are coupled with local variables and following the verification and validation with measured data from almost 300 spatially distributed monthly temperature data, the significance of climate change impact is presented with relevant interpretations. The above explanations note that the greatest change in the last 40 years in terms of temperature is more visible in the highest temperatures rather than the average and lowest temperatures. Generally speaking, because the highest heats are observed in summer times (less rainfall month), one may conclude that there is rise in the number of drought incidents in these times in Istanbul and different cities of Turkey.

Daily peak ozone forecast in Istanbul

In this study, we attempt to develop an ozone forecast model using two different approaches. The first approach is to use a multiple linear regression method and the second is to use a feed-forward artificial neural network. Models are developed for the ozone period of April through to September of the years 2002 and 2003 and verified for May to August 2004. In both models, 19 predictors are used. Calculated agreement indices (AI) for the model development period are 0.82 for the linear regression model and 0.88 for the artificial neural network model. On the other hand, AI values decrease to 0.53 and 0.64 for the validation period. Poor performance of the models in the validation phase might be due to the different maximum daily ozone averages of these two periods. While the average of maximum ozone values is 61.1 μg m−3 in the model development phase, it is 42.2 μg m−3 in the model validation phase.