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1. VLC-Enabled Human-Aware Building Management System

   Y, Kalay; H, Sathyanarayanan; D, Schaumann; A, Wang; G, Chen; R, G (July 2020, Proc. 22nd International Conference on Human-Computer Interaction (HCII2020))

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   “Smart” buildings that can sense and detect people’s presence have been in use for the past few decades, mostly using technologies that trigger reactive responses such as turning on/off heating/ventilating, lighting, security, etc. We argue that to be considered truly smart, buildings must become “aware” about the locations and activities of their inhabitants so they can proactively engage with the occupants and inform their decision making with respect to which actions to execute, by whom and where. To help assess the potential impact of “aware” buildings on their occupants, we are developing a multi-agent simulation-powered building management system that can sense human and building assets, extrapolate patterns of utilization, simulate what-if scenarios and suggest changes to user activities and resource allocation to maximize specific Key Performance Indicators (KPIs). The system is able to evaluate the implications of potential conflict resolution strategies and account for individual and collaborative activities of different types of users in semantically rich environments. Sensing in our case is based on Visible Light Communication (VLC) technology, embedded in a building’s LED lighting system. It can detect the actors, where they are located and what they do. To understand what happens in each space at any given time the information derived from the VLC system is combined with models of users’ activity schedules, profiles, and space affordances. We demonstrate our approach by hypothetically applying it to a Cardiac Catheterization Laboratory (CCL). The CCL is high-intensity hospital unit, second only to the Emergency Department in terms of the urgency of the cases it must handle. An aware building will help both patients and staff to allocate their (always scarce) resources more efficiently, saving time and alleviating stress. 

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2. A Deep Reinforcement Learning Based Recommender System for Occupant-Driven Energy Optimization in Commercial Buildings

   https://doi.org/10.1109/JIOT.2020.2974848  

   Wei, Peter; Xia, Stephen; Chen, Runfeng; Qian, Jingyi; Li, Chong; Jiang, Xiaofan (February 2020, IEEE Internet of Things Journal)

   In this work, we present recEnergy, a recommender system for reducing energy consumption in commercial buildings with human-in-the-loop. We formulate the building energy optimization problem as a Markov Decision Process, show how deep reinforcement learning can be used to learn energy saving recommendations, and effectively engage occupants in energy-saving actions. is a recommender system that learns actions with high energy saving potential, actively distribute recommendations to occupants in a commercial building, and utilize feedback from the occupants to learn better energy saving recommendations. Over a four week user study, four different types of energy saving recommendations were trained and learned. improves building energy reduction from a baseline saving (passive-only strategy) of 19% to 26%. 

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3. Assessment of wind-induced vibration mitigation in a tall building with damped outriggers using real-time hybrid simulations

   https://doi.org/10.17603/ds2-mj64-6s83  

   Al Subaihawi, Safwan; Kolay, Chinmoy; Marullo, Thomas; Ricles, James; Quiel, Spencer (January 2023, Designsafe-CI)

   The system under investigation is a 40 story building. Real-time hybrid simulations (RTHSs) were performed on the building, where the entire façade of the structure is subjected to wind loading over a 360 second duration. Nonlinear viscous dampers between the outrigger truss and perimeter columns are placed at stories 20th and 30th. The outcome of the tests is to assess the ability of the damped outrigger system to suppress undesirable floor accelerations. The data collected from the tests can be reused by replaying the real-time hybrid simulation offline, where all of the response quantities of the building can then be retrieved. The data can be reused to study the response of tall buildings with outriggers and passive dampers subjected to wind natural hazards. 

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4. ENENGY MANAGEMENT OF SMART COMMUNITY WITH EV CHARGING USING DISTRIBUTED MODEL PREDICTIVE CONTROL

   Zhou, Fenglin; Li, Yaoyu; Wang, Wenyi (October 2018, Proceedings of ASME 2018 Dynamic Systems and Control Conference)

   Human activities in buildings are connected by various transportation measures. For the emerging Smart and Connected Communities (S&CC), it is possible to synergize the energy management of smart buildings with the vehicle operation/travel information available from transportation infrastructure, e.g. the intelligent transportation systems (ITS). Such information enables the prediction of upcoming building occupancy and upcoming charging load of electrified vehicles. This paper presents a predictive energy management strategy for smart community with a distributed model predictive control framework, in which the upcoming building occupancy and charging load are assumed to be predictable to certain extent based on the ITS information. An illustrative example of smart community is used for simulation study based on a Modelica simulation model, in which a chilled-water plant sustains the ventilation and air conditioning of three buildings, and each building is assumed to host a number of charging stations. Simulation study is performed to validate the proposed strategy. 

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5. ENENGY MANAGEMENT OF SMART COMMUNITY WITH EV CHARGING USING DISTRIBUTED MODEL PREDICTIVE CONTROL

   Fenglin Zhou, Yaoyu Li (October 2018, Proceedings of ASME 2018 Dynamic Systems and Control Conference)

   Human activities in buildings are connected by various transportation measures. For the emerging Smart and Connected Communities (S&CC), it is possible to synergize the energy management of smart buildings with the vehicle operation/travel information available from transportation infrastructure, e.g. the intelligent transportation systems (ITS). Such information enables the prediction of upcoming building occupancy and upcoming charging load of electrified vehicles. This paper presents a predictive energy management strategy for smart community with a distributed model predictive control framework, in which the upcoming building occupancy and charging load are assumed to be predictable to certain extent based on the ITS information. An illustrative example of smart community is used for simulation study based on a Modelica simulation model, in which a chilled-water plant sustains the ventilation and air conditioning of three buildings, and each building is assumed to host a number of charging stations. Simulation study is performed to validate the proposed strategy. 

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