Building-level energy prediction and control based on BIM and IIoT technologies.

This paper presents the NOECS framework based on Building Information Modeling (BIM) and Internet of Things (IoT) technologies for the collaborative optimization of building energy efficiency and thermal comfort. The framework integrates BIM spatial semantics with multi-source IoT sensor data and utilizes Neural Ordinary Differential Equations (ODE) and Transformer models to accurately predict temperature, humidity, and Predicted Mean Vote (PMV). Reinforcement learning is applied for HVAC control optimization. The model first binds sensors and devices effectively through BIM floor plans and room partitions, organizing state vectors by region to support subsequent visualization and operational decision-making. Experimental results show that NOECS achieves a Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of approximately 1.0 °C for temperature prediction, 2.3% for humidity, and 2.7-2.9% for relative humidity. The MAE/RMSE for PMV remain below 0.2, demonstrating the model's high accuracy and stability. In terms of energy consumption optimization, NOECS achieves energy savings of 92.3% and 90.7% on the BEC and BIM-BEM datasets, respectively, with energy utilization rates of 93.8% and 89.3%, significantly outperforming traditional baseline models like CNN-LSTM. Additionally, NOECS demonstrates superior computational performance: the training time on the BEC dataset is 24.5s with an inference time of 118.7 ms and a parameter count of 228.3 M, while on the BIM-BEM dataset, training takes 26.7s, inference is 124.4ms, and the parameter count is 232.7 M. Compared to DERN and CNN-LSTM, the computational cost is significantly reduced. Overall, NOECS achieves remarkable results in energy conservation and comfort assurance through efficient prediction and control optimization, while exhibiting excellent deployment feasibility, offering a replicable technical path for intelligent building energy optimization.