Optimal Predictive Maintenance Scheduling for Photovoltaic Systems Using Weather Features and Hybrid Machine Learning: A Case Study of the 10 MW Tozeur PV Power Plant

Domaine:

environment and energy

Type de record:

paper

Créateur:

BecManFao

Éditeur:

Spr

Hôte:

Abstract The global transition to renewable energy necessitates efficient maintenance strategies for photovoltaic (PV) systems. This study optimizes maintenance scheduling for the 10 MW Tozeur PV power plant using a novel, integrated approach. Seasonal variations in PV system efficiency and power output are initially modeled using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Subsequently, weather-induced anomalies impacting system performance are identified through autoencoder-based anomaly detection. A hybrid forecasting model, combining Autoregressive Integrated Moving Average (ARIMA) and Isolation Forest algorithms, predicts periods of abnormal system behavior. Finally, a Random Forest Classifier integrates weather data and predicted anomalies to create a predictive maintenance schedule. This integrated methodology enhances maintenance reliability and demonstrates the potential of predictive analytics for optimizing the operational efficiency and lifespan of large-scale PV systems.

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