Machine learning for energy conversion prediction and photovoltaic-on grid protection system using IoT

Abstract

The advancement of photovoltaic (PV) systems in tropical regions faces significant efficiency challenges due to fluctuating panel surface temperatures. This study addresses these issues by implementing machine learning (ML) models, specifically k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), to classify and monitor panel temperatures. To enhance system resilience, an internet of things (IoT) based on-grid protection system was developed, featuring a dual-relay redundancy mechanism that triggers an automated trip when the current exceeds 1.30 A. This integration ensures the protection of both the PV infrastructure and household electrical loads. Experimental results demonstrate that the KNN model exhibits superior reliability with a testing accuracy of 93% and a baseline performance of 96.67%, successfully identifying both normal (25 °C to 35 °C) and high-temperature (36 °C to 48 °C) states. In contrast, while the XGBoost model reached a maximum validation accuracy of 94.44% during training, it only achieved a testing accuracy of 84% and showed significant limitations in detecting normal temperature patterns. Beyond classification, the IoT framework proved highly precise in real-time energy monitoring, with sensor error rates below 2%. This research offers a strategic solution for optimizing energy conversion and system reliability, providing a robust framework for sustainable clean energy management in tropical climates.