http://hdl.handle.net/11508/11930 2026-04-29T12:55:40Z 2026-04-29T12:55:40Z Aksoy, Ufuk Teoman Ekici, Betül Bektaş http://hdl.handle.net/11508/8740 2017-11-30T16:26:23Z 2013-12-01T00:00:00Z

TS 825 İklimsel verilerinin farklı derece gün bölgeleri için uygunluğunun değerlendirilmesi Aksoy, Ufuk Teoman; Ekici, Betül Bektaş Solar radiation is an important parameter which is used in building energy simulations especially in solar heat gain calculations of windows. Although the climatic parameters are considered comprehensively in Building Energy Performance Regulations and its appendix BEP-TR calculation method, TS 825 Thermal Insulation Requirements for Buildings is still in effect and proposes the usage of the same solar radiation data in different climatic regions. In this study the solar radiations of vertical surfaces were calculated numerically for sample cities from each of the degree day regions according to TS 825 (Antalya (1st region), Istanbul (2nd region), Elazığ (3rd region) and Kayseri (4th region)). The annual heating energy requirements of buildings having three different form factors and nine different transparency ratios were calculated with the calculated solar radiation and long-term monthly average outdoor temperature values obtained from Turkish State Meteorological Service for four different cities. The obtained energy requirements were compared with annual heating energy requirements of the same building samples calculated with the climatic data given by TS 825 and the appropriateness of the standard for different degree day regions was evaluated.

2013-12-01T00:00:00Z Bektaş Ekici, Betül http://hdl.handle.net/11508/8077 2017-11-30T16:29:32Z 2014-01-18T00:00:00Z

A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems Bektaş Ekici, Betül Accurate prediction of daily solar insolation has been one of the most important issues of solar engineering. The amount of solar insolation on a given location is a vital data for photovoltaic plants. Systems efficiency is easily affected by the changes in solar radiation so, this study is aimed to develop a Least Squares Support Vector Machine (LS-SVM) based intelligent model to predict the next day’s solar insolation for taking measures. Daily temperature and insolation data measured by Turkish State Meteorological Service for three years (2000–2002) were used as training data and the values of 2003 used as testing data. Numbers of the days from 1st January, daily mean temperature, daily maximum temperature, sunshine duration and the solar insolation of the day before parameters have been used as inputs to predict the daily solar insolation. The simulations were carried out with SVM Toolbox of MATLAB software. As a conclusion the results show that LS-SVM is a good method in estimating the amount of solar insolation of a given location with 99.294% accuracy.

2014-01-18T00:00:00Z