Machine learning-based monitoring and performance analysis of a laboratory solar photovoltaic system using an internet of things framework.
| dc.contributor.author | Mbabali, Frank | |
| dc.date.accessioned | 2026-10-12T11:12:35Z | |
| dc.date.available | 2026-10-12T11:12:35Z | |
| dc.date.issued | 2026 | |
| dc.description | Dissertation | |
| dc.description.abstract | Solar photovoltaic (PV) systems are increasingly important for electricity generation in Uganda, but their performance varies with environmental and operational conditions, while existing monitoring provides limited integration of real-time sensing and advanced data analysis. This experimental study aimed to design and implement an Internet of Things (IoT)enabled monitoring framework integrated with machine learning for real-time monitoring and performance analysis of a laboratory PV system. Specifically, the study sought to: (i) design and implement an IoT system for real-time acquisition, transmission and monitoring of environmental and electrical parameters; (ii) develop and evaluate exploratory XGBoost and LSTM tasks for PV power prediction and time-series forecasting; and (iii) evaluate relationships between environmental conditions and directly measured electrical performance. The experimental platform comprised a 40 W PV module, ESP32 microcontroller, seven environmental and electrical sensors, Wi-Fi/MQTT communication and HiveMQ cloud storage. Data were acquired at approximately 5-second intervals across five monitoring sessions between May and August 2026, producing 1,729 valid observations, of which 1,001 were used for analysis. XGBoost was evaluated using a chronological cross-session holdout and linear-regression baseline, while LSTM was retained as an exploratory time-series prototype; graphical and statistical analyses were conducted. The IoT framework successfully acquired, transmitted, stored and visualized monitoring data. However, XGBoost did not generalize to an unseen session (R² = −0.571), while the linear baseline also performed poorly (R² = −0.109), indicating insufficient data diversity for deployable prediction. The irradiance channel was not calibrated, and the battery-buffered topology prevented validation of conventional instantaneous efficiency and performance-ratio measures. The study concludes that the IoT architecture provides a functional foundation for laboratory PV monitoring, but the machine-learning and performance indicators remain exploratory. Future work should recalibrate irradiance sensing, extend monitoring to multi-week or multi-season periods, log battery state of charge, and validate fault detection using labelled fault events before largerscale deployment. | |
| dc.description.sponsorship | Prof. Tumutegyereize Peter ; Prof. Okou Richard ; Busitema University | |
| dc.identifier.citation | Mbabali, F. (2026). Machine learning-based monitoring and performance analysis of a laboratory solar photovoltaic system using an internet of things framework. [Unpublished dissertation]. Busitema University. | |
| dc.identifier.uri | https://bdears.busitema.ac.ug/handle/123456789/10568 | |
| dc.language.iso | en | |
| dc.publisher | Busitema University | |
| dc.title | Machine learning-based monitoring and performance analysis of a laboratory solar photovoltaic system using an internet of things framework. | |
| dc.type | Other |