  Most solar applications and systems can be reliably used to generate
electricity and power in many homes and offices. Recently, there is an increase
in many solar required systems that can be found not only in electricity
generation but other applications such as solar distillation, water heating,
heating of buildings, meteorology and producing solar conversion energy.
Prediction of solar radiation is very significant in order to accomplish the
previously mentioned objectives. In this paper, the main target is to present
an algorithm that can be used to predict an hourly activity of solar radiation.
Using a dataset that consists of temperature of air, time, humidity, wind
speed, atmospheric pressure, direction of wind and solar radiation data, an
Artificial Neural Network (ANN) model is constructed to effectively forecast
solar radiation using the available weather forecast data. Two models are
created to efficiently create a system capable of interpreting patterns through
supervised learning data and predict the correct amount of radiation present in
the atmosphere. The results of the two statistical indicators: Mean Absolute
Error (MAE) and Mean Squared Error (MSE) are performed and compared with
observed and predicted data. These two models were able to generate efficient
predictions with sufficient performance accuracy.
