As significant electricity consumers, buildings offer a notable potential for demand-side flexibility through advanced heating, ventilation, and air conditioning (HVAC) systems. A heat pump (HP), critical for building electrification and decarbonization, combined with active Thermal Energy Storage (aTES), especially using Phase Change Materials (PCM), can effectively shift electrical loads to alleviate grid stress during peak demand periods. This study evaluated an integrated HP-aTES system controlled by economic model predictive control (eMPC) via simulations using a Spawn of EnergyPlus framework across diverse climates in the United States (i.e., Atlanta, GA, Buffalo, NY, New York City, NY, and Tucson, AZ), aiming to minimize operating costs through load shifting while ensuring occupant thermal comfort. The studied HP-aTES system utilized a commercial-off-the-shelf water-to-air heat pump in parallel with a PCM-based thermal storage tank to explore their synergistic effects on cost savings, energy flexibility, and grid responsiveness through advanced controls in cooling applications. The simulation results demonstrated the HP-aTES system’s considerable potential, consistently maintaining comfort while achieving significant peak load shifting, exceeding 80% in climates such as Atlanta, GA, Buffalo, NY, and New York City, NY, with prediction horizons of 9–12 hours, and up to 70% in Tucson, AZ. Operating cost savings were highly dependent on utility tariffs, exceeding 40% in high-incentive regions such as New York City, NY, and Atlanta, GA, but remained around 12% under flat rates like those in Tucson, AZ. This was primarily achieved through load shifting rather than an absolute reduction in energy. Furthermore, this study confirms eMPC’s effectiveness for unlocking energy flexibility, emphasizing the crucial role of a sufficient controller prediction horizon and tariff design, and establishes a virtual testbed for future research into sensing, simplified controls, and validation.

Large language models (LLMs) are gaining attention due to their potential to enhance efficiency and sustainability in the building domain, a critical area for reducing global carbon emissions. Built on transformer architectures, LLMs excel at text generation and data analysis, enabling applications such as automated energy model generation, energy management optimization, and fault detection and diagnosis. These models can potentially streamline complex workflows, enhance decision-making, and improve energy efficiency. However, integrating LLMs into building energy systems poses challenges, including high computational demands, data preparation costs, and the need for domain-specific customization. This perspective paper explores the role of LLMs in the building energy system sector, highlighting their potential applications and limitations. We propose a development roadmap built on in-context learning, domain-specific fine-tuning, retrieval augmented generation, and multimodal integration to enhance LLMs’ customization and practical use in this field. This paper aims to spark ideas for bridging the gap between LLMs capabilities and practical building applications, offering insights into the future of LLM-driven methods in building energy applications.

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.