Deep Learning-Based Detection and Forecasting of Performance Losses in Solar PV Systems Using Multi-Sensor Data

Abstract

Photovoltaic (PV) systems are subject to nonlinear performance degradation caused by operational and environmental factors, which limits reliable energy production. Most existing studies focus on power output forecasting and fail to isolate intrinsic efficiency losses from meteorological variability. This study proposes a degradation-aware deep learning framework for predicting PV performance loss using multi-sensor time-series data. Performance degradation is formulated as a reference-based performance loss ratio derived from the deviation between observed power output and an ideal physics-informed reference model. A hybrid convolutional neural network (CNN) and long short-term memory (LSTM) architecture is employed to jointly capture local feature representations and long-term temporal degradation dynamics. Model evaluation is conducted using a synthetically generated yet physically consistent dataset, informed by real PV measurements to ensure real-world relevance. Experimental results demonstrate that the proposed CNN–LSTM model outperforms baseline approaches, including persistence, linear regression, and XGBoost, particularly in terms of mean absolute error (MAE) and normalized root mean square error (RMSE). Additional analyses confirm stable error behavior and temporal generalization, highlighting the suitability of the proposed approach for degradation-aware performance monitoring and predictive maintenance in PV systems.