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1. 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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2. Optimal control and performance of photovoltachromic switchable glazing for building integration in temperate climates

   https://doi.org/10.17863/cam.715  

   Favoino, Fabio ; Fiorito, Francesco ; Cannavale, Alessandro ; Ranzi, Gianluca ; Overend, Mauro ; -, Apollo University ; of, University Cambridge ( January 2016 , EastWest Design and Test Symposium)

   The development of adaptive building envelope technologies, and particularly of switchable glazing, can make significant contributions to decarbonisation targets. It is therefore essential to quantify their effect on building energy use and indoor environmental quality when integrated into buildings. The evaluation of their performance presents new challenges when compared to conventional “static” building envelope systems, as they require design and control aspects to be evaluated together, which are also mutually interrelated across thermal and visual physical domains. This paper addresses these challenges by presenting a novel simulation framework for the performance evaluation of responsive building envelope technologies and, particularly, of switchable glazing. This is achieved by integrating a building energy simulation tool and a lighting simulation one, in a control optimisation framework to simulate advanced control of adaptive building envelopes. The performance of a photovoltachromic glazing is evaluated according to building energy use, Useful Daylight Illuminance, glare risk and load profile matching indicators for a sun oriented office building in different temperate climates. The original architecture of photovoltachromic cell provides an automatic control of its transparency as a function of incoming solar irradiance. However, to fully explore the building integration potential of photovoltachromic technology, different control strategies are evaluated, from passive and simple rule based controls, to optimised rule based and predictive controls. The results show that the control strategy has a significant impact on the performance of the photovoltachromic switchable glazing, and of switchable glazing technologies in general. More specifically, simpler control strategies are generally unable to optimise contrasting requirements, while more advanced ones can increase energy saving potential without compromising visual comfort. In cooling dominated scenarios reactive control can be as effective as predictive for a switchable glazing, differently than heating dominated scenarios where predictive control strategies yield higher energy saving potential. Introducing glare as a control parameter can significantly decrease the energy efficiency of some control strategies, especially in heating dominated climates. 

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3. A Global Building Occupant Behavior Database

   https://doi.org/10.1038/s41597-022-01475-3  

   Dong, Bing ; Liu, Yapan ; Mu, Wei ; Jiang, Zixin ; Pandey, Pratik ; Hong, Tianzhen ; Olesen, Bjarne ; Lawrence, Thomas ; O’Neil, Zheng ; Andrews, Clinton ; et al ( December 2022 , Scientific Data)

   Abstract This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting. 

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4. Simulation-powered smart buildings management enabled by visible light communication

   3. Y. E. Kalay, H. Sathyanarayanan ( May 2020 , Proc. Simposium on Simulation in Architecture + Urban Design (SimAUD))

   null (Ed.)

   Throughout history, buildings have been considered passive containers in which occupants’ activities take place. New sensing technologies enable buildings to detect people presence and behavior. At present, this information is mostly used to trigger reactive responses, such as heating and cooling operations. We argue that truly smart environments can leverage sensed information to proactively engage with the occupants and inform decision making processes with respect to which activities to execute, by whom and where. Such ability will transform buildings from passive to active partners in the daily lives of their inhabitants. It stems from the omniscience of sensor-equipped buildings that will “know” all that is happening everywhere within (and around) them at any given moment and can predict, through simulation, the expected consequences of alternative operational decisions. Such ability is mostly relevant for hospitals and other complex buildings, where actions taken in one part of the building may affect activities in other parts of the building. We are developing a simulation-powered building management system that resolves space, actor and activity-based conflicts while harnessing data collected via visible light communication. We demonstrate this approach in a case study in the catheterization lab of a major hospital. 

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5. Learning from Non-Experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes

   https://doi.org/10.1145/3491241  

   Codispoti, Jackson ; Khamesi, Atieh R. ; Penn, Nelson ; Silvestri, Simone ; Shin, Eura ( February 2022 , ACM Transactions on Cyber-Physical Systems)

   With the acceleration of ICT technologies and the Internet of Things (IoT) paradigm, smart residential environments , also known as smart homes are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems, and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition . Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert , always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this paper, we develop a SAL algorithm, called K -Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness , representativeness , and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency. 

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