Information about human presence in indoor spaces is crucial for building energy optimization. While there has been a considerable amount of research on using neural networks to automatically detect occupancy from CO2 sensors, their application in practice is limited due to the scarcity of labeled training data. In this paper, we propose Coddora, an off-the-shelf deep learning model pretrained on data from randomized room simulations. Coddora enables quick adaptation to real-world rooms, requiring only minimal data collection. Our contribution includes two model variants for application via fine-tuning or zero-shot classifying, as well as the synthetic dataset providing data from simulations with 100,000 room models.

Complex objects are often described by several attributes of arbitrary types. To calculate the similarity between complex objects, we propose the CoSim process, which provides a composed similarity function. CoSim applies existing similarity functions for each data type of the object. To calculate the overall similarity, we weight these type-specific similarities. We demonstrate the practical application of our approach by using a simple example of objects. The example involves numerical and categorical attributes. However, our presented idea is applicable to objects with attributes of arbitrary types.