Igor Wolf

Optimizing the performance of heating, ventilation, and air-conditioning (HVAC) systems is critical in today’s energy-conscious world. Fan coil units (FCUs) play a critical role in providing comfort in various environments as an important component of HVAC systems. However, FCUs often experience failures that affect their efficiency and increase their energy consumption. In this context, deep learning (DL)-based fault detection offers a promising solution. By detecting faults early and preventing system failures, the efficiency of FCUs can be improved. This paper explores DL models as fault detectors for FCUs to enable smarter and more energy-efficient hotel buildings. We tested three contemporary DL modeling approaches: convolutional neural network (CNN), long short-term memory network (LSTM), and a combination of CNN and gated recurrent unit (GRU). The random forest model (RF) was additionally developed as a baseline benchmark. The fault detectors were tested on a real-world dataset obtained from the sensory measurement system installed in a hotel and additionally supplemented with simulated data via a physical model developed in TRNSYS. Three representative FCU faults, namely, a stuck valve, a reduction in airflow, and an FCU outage, were simulated with a much larger dataset than is typically utilized in similar studies. The results showed that the hybrid model, integrating CNN and GRU, performed best for all three observed faults. DL-based fault detectors outperformed the baseline RF model, confirming these solutions as viable components for energy-efficient hotels.

An effective approach to increasing energy efficiency in buildings without compromising thermal comfort is to optimize heating, ventilation, and air conditioning (HVAC) systems through the use of advanced building-management system features, such as fault detection and diagnosis. Such functions are usually developed based on simulation models that must be calibrated and validated to achieve an appropriate level of accuracy and reliability. The objective of this study was to develop and calibrate a room-level simulation model of a hotel building and its HVAC system using TRNSYS 18 software and real data collected from the smart room system installed in the building. The calibration process was performed with 100 rooms using 5-min samples of room temperatures in selected 1-month periods during the summer and winter seasons by minimizing the root mean squared error (RMSE) in the average of the observed rooms using a genetic algorithm. The calibrated model was able to predict room temperatures with an RMSE of 0.79 ± 0.14 °C and a coefficient of variation in the root mean squared error (cvRMSE) of 3.58 ± 0.7%, which is well below the limits prescribed by international guidelines. The model was then applied to detect faults in the operation of fan coil units in the rooms based on the residual analysis and defined if–then rules. The results obtained show that the model can track the trends of temperature changes in real conditions and successfully detect major anomalies in a system.