In light of the rapidly increasing global energy demand for cooling, energy-efficient cooling methods should be investigated. We use chiller-assisted nocturnal radiative cooling with photovoltaic-thermal collectors to cool a single-family house in Central European summer climate. A model predictive controller chooses between active and passive operation to cool a radiant slab at night to stay below an upper zone temperature limit during the following day. This goal is achieved with a high energy-efficiency ratio, compared to a conventional cooling system, while keeping the computational demands of the model predictive controller low.

The study examines the impact of variable air volume (VAV) damper stuck faults on the system operation, building indoor conditions, and reheating energy consumption. This study includes both experimental and simulation studies for five test scenarios, including a fault-free scenario. We implemented VAV damper stuck fault through the building automation system (BAS). Results show that a damper stuck in a high opening position (60% damper opening) results in supplying an excessive amount of cold air from the rooftop unit (RTU) to the conditioned zone, increasing reheating energy consumption. The results of this research can serve as a foundational resource for developing fault detection algorithms.

This study presents a real-life implementation of online integrated control that simultaneously accounts for the dynamic relationships among an outdoor environment, a thermal zone, a heat pump (HP), and a photovoltaic (PV). The integrated control successfully finds the control variables of the HP (supply airflow rate, setpoint temperature) based on federated three ANN models that predict the thermal zone’s temperature, electricity produced by the PV, and energy consumption by the HP. Compared to the local control that determines control variables based on local information (room air temperature), the integrated approach could achieve an average 53.2% energy reduction.

In the past, researchers have published many studies investigating the usefulness of reducing condenser loop flow in chiller systems. Some authors broadly recommended reducing condenser loop flow rate to save system power, while others claimed that reducing condenser loop flow rate was not always advisable. This study aims to compare results from two widely used chiller model calibration methods. A model of a real system was built and validated in Modelica, and test simulations were performed as part of a case study. Presented results for several 24-hour test periods indicate that in this particular system reduced condenser loop flow generally causes an increase in system power, and even when condenser loop flow is optimized, savings compared to a baseline are 1.5% or lower.

A novel learning-based fault detection and diagnosis (FDD) strategy utilizing a Gaussian mixture model (GMR) has been proposed to overcome the limitations of conventional FDD methods, which struggle with unidentified faults. This approach adapts to incorporate information about unknown faults. The method was tested using data from a passive chilled beams (PCB) system, an area with limited FDD research. The model was initially trained with winter data and later evaluated with spring and summer data. Faults specific to PCB systems were identified from literature and practical experience and simulated using building automation software. A feature selection method was employed to distinguish between normal and faulty operations. The GGMR FDD model was developed using winter data for known faults and normal operations. When encountering new fault data classified as unknown, the model evolves by updating and adding Gaussians. This process enables the incorporation of the new fault information. The model's performance was initially tested with winter data and then across different seasons. While the static model was less effective during the spring and summer due to changes in system features, the evolved model demonstrated high efficacy in adapting to new fault patterns and ensuring accurate fault diagnosis. The evolved model achieved over 92% fault prediction accuracy for spring and summer, highlighting its adaptability and effectiveness in varied conditions.