Photovoltaic Digital Twins: Mathematical Modeling vs. Neural Networks for Energy Management in Smart Buildings

Efficiently controlling and managing photovoltaic installations for self-consumption is key to making optimal use of these systems. The first step to achieving that control is modeling the generation of electricity by photovoltaic panels with digital twins. This paper presents an experimental evaluation of four photovoltaic power prediction models in a real-world environment using high-resolution, annual data from a university facility. A mathematical model and three neural network models (MLP, LSTM, and GRU) are compared under standardized metrics. The results demonstrate that the Multilayer Perceptron (MLP) achieves the highest overall accuracy and seasonal consistency. It outperforms the other three models, particularly in scenarios where the relationships between meteorological variables and power generation are predominantly static. However, the mathematical model maintains a competitive performance in conditions of low variability, such as in the summer and autumn. The seasonal analysis reveals the importance of selecting and adjusting models according to operational and climatic contexts. This study’s main contribution is identifying the MLP as the most efficient option for implementing digital twins in photovoltaic installation control. This facilitates real-time monitoring, optimization, and predictive maintenance and contributes to smart, sustainable energy management in buildings to align with climate neutrality guidelines.