Real-time IoT-Based Monitoring and ANN-Driven Prediction of Electrical Parameters in a 1200 W Photovoltaic System

Eko Supriyanto; Abu Hasan; Tulus  Pramuji; Tri Raharjo  Yudantoro; Khamami; Roni Apriantoro; Hanny Nurrani

Authors

Eko Supriyanto Politeknik Negeri Semarang
Abu Hasan Electrical Engineering Department, Politeknik Negeri Semarang, Central Java, 50275, Indonesia
Tulus Pramuji Electrical Engineering Department, Politeknik Negeri Semarang, Central Java, 50275, Indonesia
Tri Raharjo Yudantoro Electrical Engineering Department, Politeknik Negeri Semarang, Central Java, 50275, Indonesia
Khamami Electrical Engineering Department, Politeknik Negeri Semarang, Central Java, 50275, Indonesia
Roni Apriantoro Electrical Engineering Department, Politeknik Negeri Semarang, Central Java, 50275, Indonesia
Hanny Nurrani Electrical Engineering Department, Politeknik Negeri Semarang, Central Java, 50275, Indonesia

Keywords:

Artificial neural network (ANN), Internet of Things (IoT), MQTT protocol, photovoltaic monitoring

Abstract

The increasing adoption of small-scale photovoltaic (PV) systems underscores the necessity for advanced monitoring that improves dependability and maintenance efficacy. Conventional photovoltaic monitoring emphasizes real-time data acquisition but is deficient in predictive capabilities, hindering early defect identification and proactive maintenance. This study introduces an IoT-enabled real-time monitoring system utilizing ANN-based predictions for a 1200 W photovoltaic configuration. Voltage and current sensors interface with an ESP32 microcontroller to quantify and analyze voltage, current, power, and energy. Data is transmitted to the cloud using MQTT, enabling users to remotely monitor and display information through web or mobile applications. A feedforward Artificial Neural Network (ANN) taught by backpropagation enhances intelligence by predicting future electrical performance based on historical data. Experimental findings indicate a consistent communication latency of 1-2 seconds and sensor inaccuracies below 3%. The ANN model attained a MAPE of 3-4%. The integration of IoT monitoring with ANN prediction facilitates early anomaly detection, enhances operational understanding, and enables scalable, intelligent energy management.

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