Coşkun Hamzaçebi

Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting

Artificial neural network is a valuable tool for time series forecasting. In the case of performing multi-periodic forecasting with artificial neural networks, two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in order to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result.

Evaluation of process parameters for lower surface roughness in wood machining by using Taguchi design methodology

This paper presents a study of the Taguchi design method for obtaining lower surface roughness values in terms of process parameters in wood machining. The process parameters considered were feed rate, cutting depth, number of knives, annual ring (earlywood–latewood) and grit number of abrasive. The settings of the process parameters were determined by using Taguchi experimental design method. Orthogonal arrays of Taguchi and the signal-to-noise (S/N) ratio were employed to find the optimal levels and to analyze the effect of process parameters on surface roughness. In addition, the Pareto ANOVA analysis was used in order to measure the influence of each process parameter on surface roughness. The results of Taguchi analysis revealed that the most significant variable on surface roughness of both beech and spruce woods by S/N ratio analysis and Pareto ANOVA analysis is the grit number of abrasive. It was also understood that the Taguchi design technique is very suitable to solve the surface quality problem regarding machining of wood species.

Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network

Abstract Electricity is one of the most important end-user energy types in today’s world and has an effective role in development of societies and economies. Stability of electricity supply is provided by matching of generated and consumed electricity amount during the all-day. So, electricity consumption forecasting is an essential issue for electric utilities. In this study, the monthly electricity demand of Turkey has been predicted. To model the effects of seasonality and trend, four different ANN models have been developed and selected the superior one. In addition, the selected ANN model has been compared with SARIMA model in order to increase the acceptability and reliability of the ANN model. The monthly electricity demand of Turkey has been predicted between 2015 and 2018 via the ANN model that can make successful and high-accuracy predictions according to the performance measures. The forecasting values will help in determining the medium-term and stable energy policies.

Assessing the logistics activities aspect of economic and social development

Logistics is concerned with the coordination and planning of products, services and information flow between the points of production and consumption. The logistics sector, contributes directly to production and consumption activities, has a critical importance for social and economic development. In this study, Turkeys logistics map was created in order to distribute the logistics burden in the cities which have intensive logistics activities and improve logistics ability in the cities which have poor logistics activities. Firstly, social and economic variables that affect the logistics operations of cities of Turkey were investigated. The identified variables were reduced to a single factor which is called logistics via factor analysis and the clustering analysis was performed by using the logistics factor. In clustering analysis, k-means method was used and the cities of Turkey clustered from strong to weak according to logistics activities. The obtained results were analysed and interpreted.