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Open Access Publications from the University of California

Scholarly Works (9 results)

  • Article
  • Peer Reviewed
Building Efficiency and Sustainability in the Tropics (SinBerBEST) (2018)
Smart buildings today are aimed at providing safe, healthy, comfortable, affordable, and beautiful spaces in a carbon and energy-efficient way. They are emerging as complex cyber-physical systems with humans in the loop. Cost, the need to cope with increasing functional complexity, flexibility, fragmentation of the supply chain, and time-to-market pressure are rendering the traditional heuristic and ad hoc design paradigms inefficient and insufficient for the future. In this paper, we present a platform-based methodology for smart building design. Platform-based design (PBD) promotes the reuse of hardware and software on shared infrastructures, enables rapid prototyping of applications, and involves extensive exploration of the design space to optimize design performance. In this paper, we identify, abstract, and formalize components of smart buildings, and present a design flow that maps high-level specifications of desired building applications to their physical implementations under the PBD framework. A case study on the design of on-demand heating, ventilation, and air conditioning (HVAC) systems is presented to demonstrate the use of PBD.
    Cover page: Design Automation for Smart Building Systems
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2018)

    With the deployment of large sensor-actuator networks, Cyber-Physical Systems (CPSs), such as smart buildings, smart grids, and transportation systems, are producing massive amounts of data often in different forms and quality. These data are in turn being used collectively to inform decision-making of the entities that engage with the CPSs. The impact of these systems on people's lives has led to a strong call for accountability of system decisions made based upon various data sources. The collection, analysis, and dissemination of these data also present a privacy risk that needs to be addressed.

    The first part of this dissertation focuses on accountable data fusion. We develop an online prediction framework that integrates dynamic sensor measurements with prior knowledge. The proposed framework facilitates reasoning about prediction confidence, which is crucial to making dependable decisions. We also move beyond predictive modeling to interpretable analytics by evaluating the influence of each data instance on the algorithmic outcome. We formalize the notion of ``data value,'' and provide efficient algorithms to compute it. This value notion not only enables us to better understand black-box predictions through the lens of training data, but allows for fair allocation of the profit generated from a prediction model that is built with data from cooperative entities. We further use the proposed value notion to develop an effective data sanitization mechanism, which screens off low-quality or even adversarial data instances from the training set.

    In the second part, we address the problem of incorporating privacy as an active engineering constraint into the CPS design and operation. We discuss a privacy metric inspired by information theory and provide algorithms to optimize the privacy mechanism for a given system or co-design the privacy mechanism and system control. In order to avoid unnecessary privacy-utility tradeoffs, we develop a framework to identify redundant data for specific decision-making processes. Furthermore, we present a privacy-preserving data publishing system, which can achieve improved data utility by optimizing the privacy mechanism according to the use of published data. While the algorithms and techniques introduced can be applied to many CPSs, we will mainly focus on the implications for smart buildings.

      Cover page: Accountable Data Fusion and Privacy Preservation Techniques in Cyber-Physical Systems
      • Article
      • Peer Reviewed
      LBL Publications (2019)
      Energy-related occupant behaviour is crucial to design and operation of energy and control systems in buildings. Occupant behaviours are often oversimplified as static schedules or settings in building performance simulation ignoring their stochastic nature. The continuous and dynamic interaction between occupants and building systems motivates their simultaneous simulation in an efficient manner. In the past, simultaneous simulation has relied on co-simulation approaches or customized source code changes to building simulation programmes. This paper presents Buildings. Occupants, an open-source package implemented in Modelica, for the simulation of occupant behaviours of lighting, windows, blinds, heating and air conditioning systems in office and residential buildings. Examples were presented to illustrate how the models in the Occupants package are capable to simulate stochastic occupant behaviours. The major contribution of this work is to introduce the equation-based modelling approach to simulate occupant behaviours in buildings and to develop an open-source Occupants package in the Modelica language.
        Cover page: Buildings.Occupants: a Modelica package for modelling occupant behaviour in buildings
        • Article
        • Peer Reviewed
        LBL Publications (2017)
        Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce operational energy costs (with time-of-use rates) in commercial buildings. The accurate prediction of the cooling load, and the optimal control strategy for managing the charging and discharging of a TES system, are two critical elements to improving system performance and achieving energy cost savings. This study utilizes data-driven analytics and modeling to holistically understand the operation of an ice–based TES system in a shopping mall, calculating the system's performance using actual measured data from installed meters and sensors. Results show that there is significant savings potential when the current operating strategy is improved by appropriately scheduling the operation of each piece of equipment of the TES system, as well as by determining the amount of charging and discharging for each day. A novel optimal control strategy, determined by an optimization algorithm of Sequential Quadratic Programming, was developed to minimize the TES system's operating costs. Three heuristic strategies were also investigated for comparison with our proposed strategy, and the results demonstrate the superiority of our method to the heuristic strategies in terms of total energy cost savings. Specifically, the optimal strategy yields energy costs of up to 11.3% per day and 9.3% per month compared with current operational strategies. A one-day-ahead hourly load prediction was also developed using machine learning algorithms, which facilitates the adoption of the developed data analytics and optimization of the control strategy in a real TES system operation.
          Cover page: Data analytics and optimization of an ice-based energy storage system for commercial buildings
          • Article
          Building Efficiency and Sustainability in the Tropics (SinBerBEST) (2014)
            Cover page: SoundLoc: Acoustic Method for Indoor Localization without Infrastructure
            • Article
            Building Efficiency and Sustainability in the Tropics (SinBerBEST) (2014)
            We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupantcarried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.
              Cover page: Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2016)
              Estimating an occupant's location is arguably the most fundamental sensing task in smart buildings. The applications for fine-grained, responsive building operations require the location sensing systems to provide location estimates in real time, also known as indoor tracking. Existing indoor tracking systems require occupants to carry specialized devices or install programs on their smartphone to collect inertial sensing data. In this paper, we propose MapSentinel, which performs non-intrusive location sensing based on WiFi access points and ultrasonic sensors. MapSentinel combines the noisy sensor readings with the floormap information to estimate locations. One key observation supporting our work is that occupants exhibit distinctive motion characteristics at different locations on the floormap, e.g., constrained motion along the corridor or in the cubicle zones, and free movement in the open space. While extensive research has been performed on using a floormap as a tool to obtain correct walking trajectories without wall-crossings, there have been few attempts to incorporate the knowledge of space use available from the floormap into the location estimation. This paper argues that the knowledge of space use as an additional information source presents new opportunities for indoor tracking. The fusion of heterogeneous information is theoretically formulated within the Factor Graph framework, and the Context-Augmented Particle Filtering algorithm is developed to efficiently solve real-time walking trajectories. Our evaluation in a large office space shows that the MapSentinel can achieve accuracy improvement of 31.3% compared with the purely WiFi-based tracking system.
                Cover page: MapSentinel: Can the Knowledge of Space Use Improve Indoor Tracking Further?
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2018)
                Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.
                  Cover page: A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2018)
                  Smart buildings today are aimed at providing safe, healthy, comfortable, affordable, and beautiful spaces in a carbon and energy efficient way. They are emerging as complex cyber-physical systems with humans in the loop. Cost, the need to cope with increasing functional complexity, flexibility, fragmentation of the supply chain, and time-to-market pressure are rendering the traditional heuristic and ad hoc design paradigms inefficient and insufficient for the future. In this paper, we present a platform-based methodology for smart building design. Platform-based design (PBD) promotes the reuse of hardware and software on shared infrastructures, enables rapid prototyping of applications, and involves extensive exploration of the design space to optimize design performance. In this paper, we identify, abstract, and formalize components of smart buildings, and present a design flow that maps high-level specifications of desired building applications to their physical implementations under the PBD framework. A case study on the design of on-demand Heating, Ventilation, and Air Conditioning (HVAC) systems is presented to demonstrate the use of PBD. 
                    Cover page: Design Automation for Smart Building Systems
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