Abstract
Solar power is an accessible form of renewable energy, especially
in South Africa, where solar radiance is concentrated. However, this
source of energy is not constant in supply. Its stochastic nature can lead
to fluctuations in the voltage and frequency in the energy grid. Accurate
forecasting of the availability of solar power is crucial for operational
efficiency and distribution of power. Machine learning models provide a
powerful forecasting tool for solar photovoltaic power. Due to the inaccessibility
of accurate and measured solar irradiance data, this study uses
a digital twin to generate meteorological attributes to predict the solar
output power of the University of Johannesburg solar photovoltaic plant
based in Auckland Park, Johannesburg. The synthetic dataset contains
hourly data over a calendar year. The deep learning algorithms that were
used for prediction are the Long-Short Term Memory (LSTM) and Recurrent
Neural Network. The findings showed that the LSTM is the best
predictor using the Root Mean Squared Error (RMSE), Mean Squared
Error (MSE) and Mean Absolute Error (MAE) performance metrics.
This study is crucial as it supports the Sustainable Development Goals
(SDG) aim to reduce carbon emissions, provide clean energy and improve
access to electricity.