|Commenced in January 2007||Frequency: Monthly||Edition: International||Paper Count: 4|
This paper deals with the optimum tilt angle for the solar collector in order to collect the maximum solar radiation. The optimum angle for tilted surfaces varying from 0◦ to 90◦ in steps of 1◦ was computed. In present study, a theoretical model is used to predict the global solar radiation on a tilted surface and to obtain the optimum tilt angle for a solar collector in Bursa, Turkey. Global solar energy radiation on the solar collector surface with an optimum tilt angle is calculated for specific periods. It is determined that the optimum slope angle varies between 0◦ (June) and 59◦ (December) throughout the year. In winter (December, January, and February) the tilt should be 55◦, in spring (March, April, and May) 19.6◦, in summer (June, July, and August) 5.6◦, and in autumn (September, October, and November) 44.3◦. The yearly average of this value was obtained to be 31.1◦ and this would be the optimum fixed slope throughout the year.
Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs). Climatological data and measures, mainly air temperature, humidity, sunshine duration, and wind speed between 1995 and 2007 were used to design and validate a feed forward and recurrent neural network based prediction systems. In this paper we present our reference system based on a feed-forward multilayer perceptron (MLP) as well as the proposed approach based on an RNN model. The obtained results were promising and comparable to those obtained by other existing empirical and neural models. The experimental results showed the advantage of RNNs over simple MLPs when we deal with time series solar radiation predictions based on daily climatological data.
Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.