Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection

Abstract

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.

Evaluation of the Suitability of Using Artificial Neural Networks in Assessing the Effectiveness of Greywater Heat Exchangers

“…Integrating ANNs with Internet of Things sensors would make it possible to optimize their operation in real time, especially if adaptive artificial neural network models capable of adjusting to changing operating conditions were used. Experience with the application of similar techniques in the broadly understood construction sector [51,52] allows us to believe that the implementation of such a solution would not only maximize the achieved energy savings, but also significantly increase the reliability of the installation, e.g., by anticipating the maintenance needs of the heat recovery installation and examining its operational damage.…”

Integrated Simulation and Calibration Framework for Heating System Optimization

“…The optimization of heating systems can be divided into model-free approaches, which commonly employ a form of optimization through fault detection, and model-based approaches utilizing simulation models or digital twins. Recent work for the first branch incorporates the use of neural networks to detect faults of HVAC Systems [3,4]. In contrast, our approach is focused on the latter branch, employing parameter optimization instead, and aims for a general system level optimization utilizing a simulation model, a digital copy of the real heating system.…”

Analysis of the Wireless Power Transfer System Using a Finite Grid of Planar Circular Coils

“…Wireless power transfer (WPT) systems are used in, for example, electric vehicles (EVs) [2][3][4][5], data transmission [6], and consumer electronics [7,8]. WPT technology has also found its application in medical devices [9,10], robotic systems [11], and buildings equipped with sensors [12]. In the publications [13,14], various shapes of coils and the influence of their geometry on the system efficiency are analysed.…”