Quantification of how mechanical ventilation influences the airborne infection risk of COVID-19 and HVAC energy consumption in office buildings.

This paper presents an EnergyPlus-based parametric analysis to investigate the infection risk of Coronavirus Disease 2019 (COVID-19) under different mechanical ventilation scenarios for a typical medium-sized office building in various climate zones. A Wells-Riley (WR) based Gammaitoni-Nucci (GN) model was employed to quantitatively calculate the airborne infection risk. The selected parameters for the parametric analysis include the climate zone, outdoor air fraction, fraction of infectors, quanta generation rate, and exposure time. The loss and deposition of particles are not considered. The results suggest that the COVID-19 infection risk varies significantly with climate and season under different outdoor air fraction scenarios since the building heating and cooling load fundamentally impacts the supply airflow rate and thus directly influences the amount of mechanical ventilation, which determines the dilution ratio of contaminants. This risk assessment identified the climate zones that benefit the most and the least from increasing the outdoor air fraction. The climate zones such as 1A (Honolulu, HI), 2B (Tucson, AZ), 3A (Atlanta, GA), and 7 (International Falls, MN) are the most energy-efficient locations when it comes to increasing the outdoor air fraction to reduce the COVID-19 infection risk. In contrast, the climate zones such as 6A (Rochester, MN) and 6B (Great Falls, MT) are the least energy-efficient ones. This paper facilitates understanding a widely recommended COVID-19 risk mitigation strategy (i.e., increase the outdoor airflow rate) from the perspective of energy consumption. Electronic Supplementary Material: Supplementary material is available for this article at 10.1007/s12273-022-0937-5 and is accessible for authorized users.

Optimization-informed Rule Extraction for HVAC system: A Case Study of Dedicated Outdoor Air System Control in a Mixed-Humid Climate Zone.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.

Occupant health in buildings: Impact of the COVID-19 pandemic on the opinions of building professionals and implications on research.

The objectives of this study are to investigate building professionals' experience, awareness, and interest in occupant health in buildings, and to assess the impact of the COVID-19 pandemic on their opinions, as well as to compare the research on occupant health in buildings to professionals' opinions. To address these objectives, a mixed research methodology, including a thorough review of the literature (NL = 190) and an online survey (NS = 274), was utilized. In general, there is an increasing research interest in occupant health and a heightened interest in health-related projects, among professionals, following the COVID-19 pandemic. Specifically, among the nine different building attributes examined, indoor air quality was the most researched building attribute with a focus on occupant health and was also presumed to be the most important by the professionals. Professionals considered fatigue and musculoskeletal pain to be the most important physical well-being issues, and stress, anxiety, and depression to be the most important mental well-being issues that need to be the focus of design, construction, and operation of buildings to support and promote occupant health, while eye-related symptoms and loss of concentration were the most researched physical and mental well-being symptoms in the literature, respectively. Finally, professionals indicated that COVID-19 pandemic had significant effect on their perspectives regarding buildings' impact on occupant health and they believed future building design, construction and operation will focus more on occupant health because of the pandemic experience.

From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control.

Occupants are the core of the built environment. Traditional heating, ventilation, and air-conditioning (HVAC) systems operate with predefined schedules and maximum occupancy assumptions with no consideration of specific occupant information. These generalized assumptions usually do not align with the actual demand and result in over-conditioning and occupant discomfort. In recent years, with the aid of Information & Communication Technology (ICT) and Computer Science (CS), it is possible to acquire real-time and accurate occupant information to satisfy the exact thermal requirement through specific HVAC control in one particular built environment. This mechanism is called HVAC "Occupant-centric Control (OCC)." HVAC OCC strategy starts with collecting the occupant's information (e.g., presence/absence) and then applies it to meet the occupant's requirement (e.g., thermal comfort). However, even though some research studies and field pilot demonstrations have been devoted to the field of OCC, there is a lack of systematic knowledge about occupant data, which is the principal component of OCC for HVAC researchers and practitioners. To fill this gap, this review paper discusses OCC with a particular emphasis on occupant information and investigates how this information can assist HVAC operation in providing an acceptable built environment in required spaces during the required time. We provide a fine-grained, comprehensive picture of occupant information, discuss its features, the modalities of information feed-in into the HVAC control, and the application of commonly utilized occupant information for OCC.

Ten questions concerning occupant health in buildings during normal operations and extreme events including the COVID-19 pandemic.

Even before the COVID-19 pandemic, people spent on average around 90% of their time indoors. Now more than ever, with work-from-home orders in place, it is crucial that we radically rethink the design and operation of buildings. Indoor Environmental Quality (IEQ) directly affects the comfort and well-being of occupants. When IEQ is compromised, occupants are at increased risk for many diseases that are exacerbated by both social and economic forces. In the U.S. alone, the annual cost attributed to sick building syndrome in commercial workplaces is estimated to be between $10 billion to $70 billion. It is imperative to understand how parameters that drive IEQ can be designed properly and how buildings can be operated to provide ideal IEQ to safeguard health. While IEQ is a fertile area of scholarship, there is a pressing need for a systematic understanding of how IEQ factors impact occupant health. During extreme events, such as a global pandemic, designers, facility managers, and occupants need pragmatic guidance on reducing health risks in buildings. This paper answers ten questions that explore the effects of buildings on the health of occupants. The study establishes a foundation for future work and provides insights for new research directions and discoveries.

Advanced building energy management system demonstration for Department of Defense buildings.

This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole-building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources, including blueprints, reduced-order models (ROM) and measurements, and employed advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to facilities managers. A demonstration of aBEMS was conducted in buildings at Naval Station Great Lakes. The facility building management systems were extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of more efficient operation strategies. At Naval Station Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The paper concludes with recommendations for widespread aBEMS success.