Organizations such as ASHRAE and the Centers for Disease Control and Prevention (CDC) have proposed guidelines for controlling infectious aerosols in buildings, which can be met through measures such as modified operation of the heating, ventilation, and air-conditioning (HVAC) system or incorporating air-cleaning technologies. However, more research is needed to understand the trade-offs between health, energy, and comfort aspects when designing measures for these guidelines. To address this gap, this work conducts an analysis using new models for air-cleaning technologies, including in-duct and in-room germicidal ultraviolet (GUV) systems and portable air cleaners (PACs). These models are incorporated into an existing prototypical office building model and six measures are designed to meet ASHRAE Standard 241 and CDC clean air targets: MERV 13 HVAC filtration, maximum outdoor air supplied to the building, PACs, and in-duct, upper-room, and whole-room GUV. The measures are simulated for an office building in a cool and humid climate compared against a baseline simulation using MERV 8 filtration. The results show that all measures, except for the maximum outdoor air case, can meet the ASHRAE 241 standard without significant impacts on energy or comfort. The HVAC system measures were not able to meet the CDC target with the default system sizing and lead to significant energy increases, while the in-room measures were able to meet the CDC target with small impacts on energy consumption. This research provides practical guidance for building operators to meet clean air targets while limiting energy and comfort impacts.

In this paper, a novel hybrid modeling approach is proposed to to combine the advantages of physics-based and data-driven approaches for predicting the thermal behavior of the building. It incorporates an RC model and neural networks by designing custom layers and utilizing a neural network modeling technique known as “Siamese neural network”. The neural network is used to predict various time-invariant and time-varying parameters in the RC model. The modeling technique allows flexibility in the model design, simultaneous training with both time-invariant and varying parameters present, warmup period and multiple-timestep forecasting per input during the training phase, and training the model with a limited number of measured states. To validate the proposed approach, it was applied to an existing building located in South Korea, using the measured data from a single air handling unit (AHU) serving an office area located on the 5th floor. The trained model was used to predict the room air temperature for the test period. It was found that a simple RC model combined with the Siamese neural network was good enough to predict room air temperature.

This paper proposes a novel two-step method for the mixed-integer non-linear model predictive control of fifth generation district heating and cooling networks. The method is applied as a proof of concept to a small, virtual district consisting of four residential buildings and one office building and it is compared to a basic integer selection control approach. The novel controller uses detailed white-box models, developed in Modelica, for both building envelopes, thermal systems, and hydraulic components and aims to minimise the overall energy use of the district, while guaranteeing thermal comfort inside the buildings. An 8-month open-loop simulation is carried out to investigate and analyze the control behaviour. Results show that the proposed two-step method significantly outperforms the basic integer-selection control approach: it exploits the available system flexibility to minimize the energy use, and achieves good thermal comfort in the buildings.

Direct Air Capture (DAC) is a rapidly evolving technology that extracts CO2 directly from ambient air. This study focuses on integrating DAC in HVAC systems to improve indoor air quality and energy efficiency by minimizing the ventilation rate. The DAC equipment is modeled in Modelica and validated with results from the literature, which is then integrated into a typical HVAC system in Modelica Buildings library. Three case studies are conducted to investigate its performance in a winter month under different scenarios. The results show that the energy can be reduced by 16.13% with DAC while the indoor air quality is improved.