Power Prediction and Anomaly Detection Using Statistical Thresholds and Satellite-Based Solar Data in Photovoltaic Systems

Abstract

The increasing scale and complexity of photovoltaic (PV) power plants require reliable and cost-effective monitoring strategies capable of identifying operational anomalies under diverse climatic conditions. However, a challenge in this context lies in the strong dependence of many existing approaches on high-quality local solar irradiance measurements, which are often unavailable, incomplete, or economically unfeasible in real-world PV installations. This article proposes an integrated methodology for PV power prediction and anomaly detection using satellite-based solar data combined with statistical thresholding techniques. Satellite-derived meteorological datasets are used to train supervised regression models to estimate PV power, which is subsequently employed as an external and physically consistent reference for anomaly detection. Monthly characteristic curves are constructed from the predicted power using statistical measures, enabling the definition of adaptive operating thresholds that account for seasonal and intraday variability. Anomalies are identified based on both statistical deviations from these dynamic limits and physically inconsistent behaviors between irradiance and measured power, such as power drops under increasing irradiance conditions. The methodology is evaluated using two independent satellite datasets with different temporal and spatial characteristics, allowing a comparative analysis of linear and tree-based machine learning models in terms of prediction accuracy, robustness, and explainability. In this way, results demonstrate that satellite-based power prediction provides a stable and scalable baseline for anomaly detection, particularly suitable for PV plants with limited instrumentation or without local irradiance sensors. The proposed approach effectively captures seasonal patterns of operational stress and distinguishes different types of anomalous behavior, offering a practical and interpretable solution for large-scale PV monitoring and performance assessment.