Augmenting Knowledge for Individual NVR Prediction in Different Spatial and Temporal Cross-Building Environments

Natural ventilation is a critical method for reducing energy consumption for heating, cooling, and ventilating buildings. Recent research has focused on utilizing environmental IoT data from both inside and outside buildings for NVR prediction based on a deep learning model. To design an accurate NVR prediction model while considering individual building environments, various knowledge-sharing methods can be applied, such as transfer learning and ensemble models for cross-building prediction. However, the characteristics of learning data and model parameters should be considered when applying transfer learning and ensemble models to predict NVR with different spatial and temporal domains. In this paper, we propose a way to design an NVR prediction model for a cross-building environment by normalizing the training data, selecting transfer learning layers that are well-suited to the data environment, and augmenting NVR knowledge via ensemble methods. Based on the experimental results, we confirm that the proposed knowledge-sharing deep learning approach, while considering the normalizing of training data, the selecting transfer learning layers, and augmenting the NVR knowledge approach, can improve the accuracy up to 11.8% in the two different offices and seasons.