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Intelligent IoT Platform for Multiple PV Plant Monitoring.

Ida Bagus Krishna Yoga Utama1, Radityo Fajar Pamungkas1, Muhammad Miftah Faridh1

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This summary is machine-generated.

This study introduces an IoT platform for monitoring multiple photovoltaic (PV) plants, enhancing operations with AI-driven power generation forecasts and real-time sensor anomaly detection.

Keywords:
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Area of Science:

  • Renewable Energy Systems
  • Internet of Things (IoT)
  • Artificial Intelligence (AI)

Background:

  • The rapid expansion of the photovoltaic (PV) plant industry necessitates efficient management of geographically dispersed facilities.
  • Current operational and maintenance strategies struggle with the complexity of monitoring multiple, remote PV plants.

Purpose of the Study:

  • To develop an integrated Internet of Things (IoT) platform for centralized monitoring of multiple photovoltaic (PV) plants.
  • To enhance the platform's capabilities with predictive power generation and real-time anomaly detection functionalities.

Main Methods:

  • Implementation of an IoT platform for aggregating data from multiple PV plants.
  • Development and comparison of five Artificial Intelligence (AI) models for next-day power generation prediction.
  • Application of an adaptive threshold Isolation Forest algorithm for detecting sensor anomalies within PV plants.

Main Results:

  • The developed IoT platform successfully enables centralized monitoring of multiple PV plants.
  • The BiLSTM AI model demonstrated superior performance in power generation prediction, achieving low error metrics (MSE: 0.0072, MAPE: 0.1982, MAE: 0.0542) and high accuracy (R2: 0.9664).
  • The adaptive threshold Isolation Forest achieved high precision (0.9517) in identifying sensor anomalies.

Conclusions:

  • The proposed IoT platform provides an effective solution for managing multiple PV plants.
  • AI-driven predictions and anomaly detection significantly improve the operational efficiency and reliability of photovoltaic energy systems.