Real-Time Power Prediction for Bifacial PV Systems in Varied Shading Conditions: A Circuit-LSTM Approach Within a Digital Twin Framework
Dou Hong, Jieming Ma, Kangshi Wang, Ka Lok Man, Huiqing Wen, Prudence W. H. Wong
Abstract
The prediction of bifacial photovoltaic (bPV) system performance under variable conditions has persistently challenged researchers and practitioners alike, largely due to the unstable and imprecise irradiance measurements and the extensive training processes required for machine learning-based methods. Addressing these issues, this study introduces an innovative digital twin system that integrates a novel circuit-long short-term memory (LSTM) model with the newly proposed triangle-shading pattern estimation method, eliminating dependencies on direct irradiance measurements and historical data. Our approach uniquely combines the adaptability of LSTM networks with circuit models, facilitating real-time power prediction with unprecedented accuracy and efficiency. Comprehensive evaluations across various shading scenarios demonstrate the proposed model's superior performance, consistently reducing mean absolute error, mean squared error, and root mean squared error by over 50% compared with existing methods. This breakthrough offers a scalable, cost-effective solution for optimizing the deployment and management of bPV systems, marking a significant advancement in the field of photovoltaic research.