Abstract:
Sri Lanka is a tropical country located close to the equator with
abundant sunlight throughout the year. For efficient utilization of this
solar resource for power generation in photovoltaic (PV) systems and
agricultural modelling, prior knowledge of global solar radiation
(GSR) in the future is important. Limited availability of onsite GSR
data and the high cost are the main barriers in forecasting GSR for Sri
Lanka. As a solution this study suggests an artificial neural network
(ANN) model to forecast hourly solar radiation using weather data
and solar angles to forecast GSR in Colombo, specifically using
feedforward neural network (FFNN) trained with Levenberg-
Marquardt (LM) back propagation algorithm. Hourly weather data for
6 weather variables and two solar angles from 1st of March 2017 to
14th of February 2018 were used for training, validation and testing
the network. Input parameters and training parameters were adjusted
to identify the most accurate network configuration and the
performance of the network was measured using normalized mean
squared error (NMSE). Coefficient of determination (R2) measured to
identify the appropriateness of using weather variables and solar
angles to forecast solar radiation. The final hourly FFNN model
consists of 2 hidden layers and there are 5 neurons and 3 neurons in
each layer respectively. This model was able to forecast hourly solar
radiation with 0.0961 NMSE and the R2 was 90.39%. This implies the
capability of this model for prediction of global solar radiation when
unseen weather data input supply to the model and ensure the accuracy
of the result.
