Embedding physical neurons in physics-informed neural networks (EP-PINNs) for enhancing chiller performance prediction

Accurate chiller performance prediction is crucial for improving the energy efficiency of heating, ventilation, and air conditioning (HVAC) systems. Data-driven models commonly used to enhance chiller performance often rely on sparse data collected under restricted conditions. These models must extrapolate beyond their training data in practical applications, but they generally lack the generalization capability needed for reliable predictions outside their training range. Additionally, their limited interpretability hampers understanding of the physical processes affecting chiller performance, complicating fault identification and performance optimization. To address these issues, this study embeds physical neurons in physics-informed neural networks (EP-PINNs) to enhance chiller performance prediction. By leveraging prior physical knowledge, physical neurons are introduced and embedded into the neural network, forming a neural network architecture with intrinsic physics-based information flow. Simultaneously, simplified physical loss terms are used to guide the training process. The proposed EP-PINNs were applied to predict the performance of four different chillers, and the results demonstrated their high prediction accuracy. Compared to data-driven models, the EP-PINNs exhibited significantly improved generalization capability and interpretability. These advantages highlight the practical value of EP-PINNs in HVAC equipment performance prediction.