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Browsing by Author "Dongre, Poorvesh"

Now showing 1 - 3 of 3
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    Clustering Appliance Energy Consumption Data for Occupant Energy-Behavior Modeling
    Dongre, Poorvesh; Aldrees, Asma; Gracanin, Denis (ACM, 2021-11-17)
    Energy consumption of buildings varies significantly across buildings with similar functions and locations. Occupant behavior is one of the most significant sources of uncertainty related to energy consumption in buildings. A deeper understanding of occupant energy behavior can help in designing personalized behavior intervention strategies to save energy and predict energy consumption. This paper uses the Pecan Street dataset to cluster building occupants based on the energy they consume for each appliance in the household, and then developed load profiles for each of the clusters.
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    Modeling and Simulating Thermostat Behaviors of Office Occupants: Are Values more important than Comfort?
    Dongre, Poorvesh; Gracanin, Denis; Mohan, Shiwali; Mostafavi, Saman; Ramea, Kalai (ACM, 2022-11-09)
    Existing literature postulates thermal behaviors primarily to be a consequence of thermal discomfort. However, some thermal behaviors, such as changing the thermostat settings in an office building, can cause thermal discomfort to other occupants in the building. Given this social constraint of thermostat behaviors, our paper uses the human-building interaction (HBI) dataset to estimate the impact of office occupants’ personal values on their thermostat-up and thermostat-down behaviors using logistic regression. Our preliminary results show that personal values such as agreeableness significantly impact thermal behaviors above and beyond what can be explained by thermal comfort-related features. Finally, we also develop a data-driven agent-based model (DDABM) to understand the emergence of thermostat behaviors influenced by personal values under various environmental conditions.
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    A Tool-Based System Architecture for a Digital Twin: A Case Study in a Healthcare Facility
    Harode, Ashit; Thabet, Walid; Dongre, Poorvesh (International Council for Research and Innovation in Building and Construction, 2023-02)
    Changes in the local and global markets are forcing A/E/C/FM (Architecture, Engineering, Construction, and Facility Management) organizations to deliver more robust and innovative operational BIMs (Building Information Models). It is hypothesized that BIMs will transform from a static 3D model to a Digital Twin providing a truly digital representation of the physical asset or the building it represents. This transformation to a dynamic Digital Twin will allow the A/E/C/FM industry to visualize, monitor, and optimize operational assets and processes to support better inspection and analysis for a more efficient facility operations and maintenance. To support the adoption and implementation of Digital Twin in A/E/C/FM, the authors have defined two clear objectives. First, we discuss requirements for a functionality-based canonical architecture to create a digital twin followed by proposing two tool-based system architecture options for its implementation. Second, we use a case study approach to develop a proof-of-concept Digital Twin of an operating room in a healthcare facility using Power BI Desktop and Azure Services. The prototype aims to monitor room air quality as per INAIL (National Institute for Insurance against Accidents at Work) and ISO (International Organization for Standards) standards. Multiple sensors connected to a Raspberry Pi 4 are used to capture real-time data for various air quality parameters including temperature, humidity, airflow, particulate contamination, and Nitrous Oxide (N2O) gas. Multiple dashboards are also created to visualize, monitor, and analyze the data harnessed from the OR sensors. The implementation addresses critical issues including security, data storage, visualization, processing, data streaming, collection, and analysis. As an initial validation, the Digital Twin prototype was presented and discussed with a healthcare BIM manager. Initial feedback from the industry expert indicated that the prototype could decrease the required time to respond to facility maintenance issues such as decreased air flow due to possible obstructions.