Comparative study of supervised algorithms for topology detection of sensor networks in building energy systems

Abstract

Optimizing the operation of building energy systems holds great potential to reduce energy consumption in buildings. However, this requires detailed system information, such as the relationship of sensor data. Automatic detection of this information requires monitoring data from buildings, which is rarely available in the needed quality for automatic assignment. This study bases on 200 weeks of data collected from eight temperature sensors of a heat pump and a heat exchanger in 5-min samples. We use this data to auto-generate grey-box models to extend the data set with 500 weeks of simulated data. We train six supervised deep learning algorithms with all the data to test whether detecting connections is possible. The maximum F1 score of 94.9% compared to real-based results with a maximum of 34.2%, which is over 60% better. The advantage of the proposed approach is its independence from the low availability of real data.