DEVELOPMENT OF BACKPROPAGATION ALGORITHM FOR ESTIMATING SOLAR RADIATION: A CASE STUDY IN TURKEY
DOI:
https://doi.org/10.59277/RRST-EE.2023.3.11Keywords:
Solar radiation, Estimation, Artificial neural network, BackpropagationAbstract
Solar energy has an important role in achieving the objective of replacing fossil fuels and reducing greenhouse gas emissions with significant potential. Engineers, architects, and farmers require accurate information about solar radiation in order to develop solar energy systems. It is a common practice for meteorological services around the world to measure the duration of sunshine and air temperature. Despite this, worldwide measurements of solar radiation are extremely rare and some information is lacking. It becomes vitally critical to estimate solar radiation at sites that have not own station. In the literature, a variety of models have been developed to estimate solar radiation. The artificial neural network (ANN) model is commonly used for the estimation of global solar radiation. In this study, a backpropagation algorithm for throughout the year is generated to estimate global solar radiation in Adana by using the meteorological data obtained from Turkish State Meteorological Services. ANN model was developed by using the data for 2014, 2015, and 2016 years with the MATLAB program. The data for 2017 is used for testing the model. A comparison between the developed model and real data is performed depending on the R2 value. As a result of the study, the R2 obtained by training the data was calculated as 0.9019. The R2 value derived from test data was calculated as 0.7277. In light of these results, it can be said that the estimation study was satisfactory.
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