Design of a machine learning-based mobile application to predict solar power output using environmental parameters

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.