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.

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.

Building occupancy, one of the most important consequences of occupant behaviors, is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community. With the vast development of information technologies in the era of the internet-of-things, occupant sensing and data acquisition are not limited to a single node or traditional approaches. The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time. In this paper, we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks. The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media. On top of that, the typical building occupancy schedules are extracted with assumed people counting rules. The second approach utilizes the processed Global Positioning System (GPS) tracking data provided by social networking service companies such as Facebook and Google Maps. Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules. The results show that the extracted building occupancy schedules from different data sources (Twitter, Facebook, and Google Maps) share a similar trend but are slightly distinct from each other and hence may require further validation and corrections. To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media (TOSSM), data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM. The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.