Abstract:
The increasing demand for renewable energy highlights the critical need for accurate solar power
forecasting, especially in regions like Uganda with high solar potential. This project focuses on
developing a machine learning-based mobile application for real-time prediction of solar power
output, using the Busitema 4MW solar power station as a case study. The application integrates
environmental parameters such as solar radiation, wind speed, air temperature, and humidity to
enhance the reliability of power generation forecasts. Machine learning models will be trained and
evaluated using historical data to ensure precision, adaptability to dynamic weather conditions,
and computational efficiency.
The project addresses challenges of solar power output prediction at the Busitema 4MW solar
power station due to the varying nature of environmental parameters which affects grid stability
and operational efficiency. By providing accurate solar power output predictions, the mobile
application empowers plant operators to make data-driven decisions. The solution includes
backend API development, user-friendly interface design, and seamless integration of the
predictive model into a mobile application culminating in a robust tool for solar power output
prediction/forecasting. This initiative not only supports Uganda’s sustainable energy goals but also
advances the efficiency and reliability of solar power infrastructure.
