Mehmet Kurban

A novel 2-D model approach for the prediction of hourly solar radiation

In this work, a two-dimensional (2-D) representation of the hourly solar radiation data is proposed. The model enables accurate forecasting using image prediction methods. One year solar radiation data that is acquired and collected between August 1, 2005 and July 30, 2006 in Iki Eylul campus of Anadolu University, and a 2-D representation is formed to construct an image data. The data is in raster scan form, so the rows and columns of the image matrix indicate days and hours, respectively. To test the forecasting efficiency of the model, first 1-D and 2-D optimal 3-tap linear filters are calculated and applied. Then, the forecasting is tested through three input one output feed-forward neural networks (NN). One year data is used for training, and 2 month(from August 1,2006 to September 30,2006) for testing. Optimal linear filters and NN models are compared in the sense of root mean square error (RMSE). It is observed that the 2-D model has advantages over the 1- D representation. Furthermore, the NN model accurately converges to forecasting errors smaller than the linear prediction filter results.

Missing wind data forecasting with adaptive neuro-fuzzy inference system

In any region, to begin generating electricity from wind energy, it is necessary to determine the 1-year distribution characteristics of wind speed. For this aim, a wind observation station must be constructed and 1-year wind speed and direction data must be collected. For determining the distribution characteristics, the collected data must be statistically analyzed. The continuity and reliability of the data are quite important for such studies on the days when possible faults can occur in any part of the observation unit or on days when, the system is on maintenance, it is not possible to record any data. In this study, it is assumed that the station had not worked at some randomly chosen days and that for these days no data could be recorded. The missing data are predicted using the data that were recorded before and after fault or maintenance by an adaptive neuro-fuzzy inference system (ANFIS). It is seen that ANFIS is successful for such a study.

The effect of missing wind speed data on wind power estimation

In this paper, the effect of possible missing data on wind power estimation is examined. One-month wind speed data obtained from wind and solar observation station which is constructed at Iki Eylul Campus of Anadolu University is used. A closed correlation is found between consecutive wind speed data that are collected for a period of 15 second. A very short time wind speed forecasting model is built by using two-input and one-output Adaptive Neuro Fuzzy Inference System (ANFIS). First, some randomly selected data from whole data are discarded. Second, 10%, 20% and 30% of all data which are randomly selected from a predefined interval (3-6 m/sec) are discarded and discarded data are forecasted. Finally, the data are fitted to Weibull distribution, Weibull distribution parameters are obtained and wind powers are estimated for all cases. The results show that the missing data has a significant effect on wind power estimation and must be taken into account in wind studies. Furthermore, it is concluded that ANFIS is a convenient tool for this kind of prediction.

Solving Unit Commitment Problem Using Modified Subgradient Method Combined with Simulated Annealing Algorithm

This paper presents the solving unit commitment (UC) problem using Modified Subgradient Method (MSG) method combined with Simulated Annealing (SA) algorithm. UC problem is one of the important power system engineering hard-solving problems. The Lagrangian relaxation (LR) based methods are commonly used to solve the UC problem. The main disadvantage of this group of methods is the difference between the dual and the primal solution which gives some significant problems on the quality of the feasible solution. In this paper, MSG method which does not require any convexity and differentiability assumptions is used for solving the UC problem. MSG method depending on the initial value reaches zero duality gap. SA algorithm is used in order to assign the appropriate initial value for MSG method. The major advantage of the proposed approach is that it guarantees the zero duality gap independently from the size of the problem. In order to show the advantages of this proposed approach, the four-unit Tuncbilek thermal plant and ten-unit thermal plant which is usually used in literature are chosen as test systems. Penalty function (PF) method is also used to compare with our proposed method in terms of total cost and UC schedule.

A Comparative Study of Three Different Mathematical Methods for Solving the Unit Commitment Problem

The unit commitment (UC) problem which is an important subject in power system engineering is solved by using Lagragian relaxation (LR), penalty function (PF), and augmented Lagrangian penalty function (ALPF) methods due to their higher solution quality and faster computational time than metaheuristic approaches. This problem is considered to be a nonlinear programming-(NP-) hard problem because it is nonlinear, mixed-integer, and nonconvex. These three methods used for solving the problem are based on dual optimization techniques. ALPF method which combines the algorithmic aspects of both LR and PF methods is firstly used for solving the UC problem. These methods are compared to each other based on feasible schedule for each stage, feasible cost, dual cost, duality gap, duration time, and number of iterations. The numerical results show that the ALPF method gives the best duality gap, feasible and dual cost instead of worse duration time and the number of iterations. The four-unit Tuncbilek thermal plant which is located in Kutahya region in Turkey is chosen as a test system in this study. The programs used for all the analyses are coded and implemented using general algebraic modeling system (GAMS).

Solar Radiation Data Modeling with a Novel Surface Fitting Approach

In this work one year hourly solar radiation data are analyzed and modeled. Using a 2-D surface fitting approach, a novel model is developed for the general behavior of the solar radiation. The mathematical formulation of the 2-D surface model is obtained. The accuracy of the analytical surface model is tested and compared with another surface model obtained from a feed-forward Neural Network(NN). Analytical surface model and NN surface model are compared in the sense of Root Mean Square Error (RMSE). It is obtained that the NN surface model gives more accurate results with smaller RMSE results. However, unlike the specificity of the NN surface model, the analytical surface model provides an intuitive and more generalized form that can be suitable for several other locations on earth.

Feasible Modified Subgradient Method for Solving the Thermal Unit Commitment Problem as a New Approach

The Lagrangian relaxation- (LR-) based methods are commonly used to solve the thermal unit commitment (UC) problem which is an important subject in power system engineering. The main drawback of this group of methods is the difference between the dual and the primal solutions which gives some significant problems on the quality of the feasible solutions. In this paper, a new approach, feasible modified subgradient (F-MSG) method which does not require finding an unconstrained global minimum of the Lagrangian function and knowing an optimal value of the problem under consideration in order to update dual variables at the each iteration, is firstly used for solving the thermal UC problem. The major advantage of the proposed approach is that it guarantees the zero duality gap and convergence independently from the size of the problem. In order to discuss the advantages of this method, the four-unit Tuncbilek thermal plant, which is located in Kutahya region in Turkey, is chosen as a small test system. The numerical results show that F-MSG gives better solutions as compared to the standard LR method.

A New Approach for Next Day Load Forecasting Integrating Artificial Neural Network Model with Weighted Frequency Bin Blocks

In this study, a new method is developed for the next day load forecasting integrating Artificial Neural Network(ANN) model with Weighted Frequency Bin Blocks (WFBB). After the WFBB is applied to all data, the results obtained from this analysis are used as the inputs in the ANN structure. However, the conventional ANN structure is also used for the next day load forecasting. The forecasting results obtained from ANN structure and the hybrid model are compared in the sense of root mean square error (RMSE). It is observed that the performance and the RMSE values for the hybrid model,the ANN model with WFBB, are smaller than the values for the conventional ANN structure. Furthermore, the new hybrid model forecasts better than the conventional ANN structure. The suitability of the proposed approach is illustrated through an application to actual load data taken from the Turkish Electric Power Company in 2002.

Finsler Geometry for Two-Parameter Weibull Distribution Function

To construct the geometry in nonflat spaces in order to understand nature has great importance in terms of applied science. Finsler geometry allows accurate modeling and describing ability for asymmetric structures in this application area. In this paper, two-dimensional Finsler space metric function is obtained for Weibull distribution which is used in many applications in this area such as wind speed modeling. The metric definition for two-parameter Weibull probability density function which has shape ( ) and scale ( ) parameters in two-dimensional Finsler space is realized using a different approach by Finsler geometry. In addition, new probability and cumulative probability density functions based on Finsler geometry are proposed which can be used in many real world applications. For future studies, it is aimed at proposing more accurate models by using this novel approach than the models which have two-parameter Weibull probability density function, especially used for determination of wind energy potential of a region.

A 2 dimensional solar radiation model

In this study solar radiation data obtained from Eskisehir region is mathematically modeled using a two dimentional (2D) approach. The approach and model is novel in the literature of solar radiation modeling. The analysis is based on mathematical behavior of hourly and daily behavioral cross-sections of the 2D data. It is observed that the deviation of the hourly data within a day exhibits a Gaussian shape, and the deviation of daily data in the year has a sinusoidal behavior. The hourly behaviour of daily data is tested by single and double Gaussian source models, whereas the daily behavior of yearly data is only modeled using sinusoidal function. By this way two different equations corresponding two different surfaces are obtained. It is concluded that single-source Gaussian surface represents the data more accurate than two dimentional Gasussian surface. Consequently, a very simple but an accurate 2D model is obtained for solar radiation data.