DEVELOPMENT OF A PLATFORM FOR ANALYSIS OF PHOTOVOLTAIC DATA FOR ENHANCED PERFORMANCE

This project, titled "Development of a Platform for Analysis of Photovoltaic Data for Enhanced Performance," addresses the challenges in optimizing photovoltaic (PV) system efficiency which is often caused by dynamic environmental factors, component degradation and the limitations of traditional monitoring methods. Existing systems typically provide limited performance metrics and lack the capacity for predictive maintenance or real-time diagnostics, leading to energy losses, increased downtime, and reduced return on investment. The primary aim of this study is to design and develop a data-driven platform that collects, analyzes, and visualizes photovoltaic data and is stored using local databases e.g Access. The objective is to enhance performance, enable predictive maintenance, and support sustainable energy management. To achieve this the study employed waterfall methodology involving IoT-based data acquisition, time-series processing, for predictive analytics. Findings from the system implementation and testing revealed that the platform could accurately identify efficiency losses, detect anomalies and predict potential system underperformance with up to 92% accuracy using real and simulated PV data from Madonna University. The study recommends that future versions expand the platform’s forecasting capabilities using larger, more diverse datasets and enhance its integration with smart grid infrastructure. Furthermore, improving cross device accessibility and adding support for more exports formats would increase the platform’s utility and adoption. The modular design ensures that this solution is scalable and adaptable to other PV systems beyond Madonna University.