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Cover page of iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

(2020)

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs.

Cover page of Measuring 3D indoor air velocity via an inexpensive low-power ultrasonic anemometer

Measuring 3D indoor air velocity via an inexpensive low-power ultrasonic anemometer

(2020)

The ability to inexpensively monitor indoor air speed and direction on a continuous basis would transform the control of environmental quality and energy use in buildings. Air motion transports energy, ventilation air, and pollutants around building interiors and their occupants, and measured feedback about it could be used in numerous ways to improve building operation. However indoor air movement is rarely monitored because of the expense and fragility of sensors. This paper describes a unique anemometer developed by the authors, that measures 3-dimensional air velocity for indoor environmental applications, leveraging new microelectromechanical systems (MEMS) technology for ultrasonic range-finding. The anemometer uses a tetrahedral arrangement of four transceivers, the smallest number able to capture a 3-dimensional flow, that provides greater measurement redundancy than in existing anemometry. We describe the theory, hardware, and software of the anemometer, including algorithms that detect and eliminate shielding errors caused by the wakes from anemometer support struts. The anemometer has a resolution and starting threshold of 0.01 m/s, an absolute air speed error of 0.05 m/s at a given orientation with minimal filtering, 3.1° angle- and 0.11 m/s velocity errors over 360° azimuthal rotation, and 3.5° angle- and 0.07 m/s velocity errors over 135° vertical declination. It includes radio connection to internet and is able to operate standalone for multiple years on a standard battery. The anemometer also measures temperature and has a compass and tilt sensor so that flow direction is globally referenced regardless of anemometer orientation. The retail cost of parts is $100 USD, and all parts snap together for ease of assembly.

Cover page of Towards utilizing internet of things (IoT) devices for understanding individual occupants' energy usage of personal and shared appliances in office buildings

Towards utilizing internet of things (IoT) devices for understanding individual occupants' energy usage of personal and shared appliances in office buildings

(2020)

Energy consumption in office buildings highly depends on occupant energy-use behaviors and intervening these behaviors could function as a cost-effective approach to enhance energy savings. Current behavior-intervention techniques extensively rely on occupant-specific energy-use information at the workstation level and often ignore shared appliances. It is because an occupant typically has full responsibility for her workstation appliances energy consumption and shares the responsibility of the shared appliances energy consumption. However, understanding energy-use behavior of both workstation and shared appliances is necessary for applying appropriate behavior-intervention techniques. Despite this importance, there is still no practical and scalable method to capture personalized energy-use information of workstation and shared appliances since the conventional methods use plug-in power meters that are extremely expensive and difficult to maintain over long period of time. To address this gap, we propose a comprehensive occupant-level energy-usage approach which utilizes the data from the internet of things devices in office buildings to provide information related to energy-use behavior of workstation and shared appliances of each occupant in an economical and feasible manner. In particular, we introduce an energy behavior index which quantitatively compares individual occupants’ energy-consuming data to identify high energy consumers and inefficient behaviors. Results from an experiment conducted in an office building equipped with internet of things devices demonstrate the feasibility of the proposed approach to classify occupants to different energy-usage categories. Our proposed approach along with appropriate behavior-intervention techniques could be used to impact occupant energy-use behaviors.

Cover page of Extracting Occupants’ Energy-Use Patterns from Wi-Fi Networks in Office Buildings

Extracting Occupants’ Energy-Use Patterns from Wi-Fi Networks in Office Buildings

(2019)

Wi-Fi networks are currently considered as an efficient and economical tool for occupancy sensing in office buildings. Studies particularly indicated that these networks could be utilized to understand/predict occupants’ energy-use patterns. Despite the value that investigating this possibility could provide for the current research, it has not been well explored how energy-use pattern information could be extracted from Wi-Fi system information. In response, this study utilizes statistical analyses to investigate the correlation of Wi-Fi flows with miscellaneous electric loads (MELs) in office buildings. MELs account for more than one-third of office-building energy consumption and are the best representative of occupants’ energy-use patterns. In the pursuit of the objective, data from two offices were collected over a 3-month period of time. Results from the analyses show that an average 92 percent of MELs energy consumption could be predicted through the Wi-Fi flows in a building. This finding thereby demonstrates that occupants’ energy-use patterns are highly positively correlated to Wi-Fi flows in a building and accordingly, the information of Wi-Fi networks could be utilized to understand/interpret these patterns. This significantly contributes to the current body of research and can be used to support efforts into understanding/enhancing occupants’ energy-use behaviors. In addition, since Wi-Fi networks are a major subset of internet of things (IoT) hardware systems and IoT implementation for intelligent energy management in buildings significantly depends on occupant energy-use patterns, this research helps IoT-based efforts by displaying how these patterns could be extracted from IoT infrastructure.

Cover page of Coordinate control of air movement for optimal thermal comfort

Coordinate control of air movement for optimal thermal comfort

(2018)

Personally controlled air movement can maintain or enhance thermal comfort in warm environments and reduce energy consumption. Unlike controlling a personal fan, using a system of fans for multiple occupants is difficult as it is hard to find an appropriate fan speed setting that maximizes occupants’ satisfaction. Since limited work has been carried out on this issue, in this paper, a novel cooperative control approach for a system of fans is proposed to provide optimized air movement for multiple occupants. This is the first time that a system of fans is controlled cooperatively in the research of built environment. The proposed approach predicts airflow in a cost-effective manner by calibrating the fans in the real environment. The operation of the fans is optimized by minimizing the worst-case deviation between the actual air speed and the desired air speed, which can be determined based on either the PMV – SET model or the occupants’ feedback. This minimax-error problem is formulated as an equivalent linear programming problem which can be solved using standard methods. The proposed approach was tested in two different indoor scenarios respectively by 1) measuring air speed directly in a business conference room and 2) involving human subject surveys in a university classroom. In the first experiment, the measured air speeds after optimization are closer to the target values at all tested temperature levels (26 °C, 27.5 °C and 29 °C) indicating improved thermal comfort. In the second experiment, only 62% of the occupants (totally 34) are satisfied with slightly increased room temperature (around 26.5 °C) before optimization, while this number increased to 94% after optimization.

Cover page of Performance analysis of pulsed flow control method for radiant slab system

Performance analysis of pulsed flow control method for radiant slab system

(2018)

We present a novel pulsed flow control method (PFM) using a two-position valve to regulate the capacity of radiant slab systems. Under PFM, the on-time duration of the valve is short (compared to all prior work, e.g. 4-minute), and fixed, while the off-time varies. We present a novel, open-source, finite difference model that assesses three-dimensional transient slab heat transfer, accounting for the transient heat storage of the pipe fluid. Sensitivity analysis results indicate the dominant factors influencing energy performance of the PFM are: on-time duration; pipe diameter; and spacing. We experimentally validated both the new control strategy and model in full-scale laboratory experiments. Compared with previous intermittent control strategies (with on-time durations over 30 min), at 50% part load the PFM reduces 27% required water flow rate and increases supply to return water temperature differential. Compared with the variable temperature control method, at 50% part load the PFM reduces 24% required water flow rate. The energy performance of PFM is comparable to that of a conventional variable flow rate control. However, it has more accurate capacity control, achieves a more uniform surface temperature distribution, and reduces initial investment by substituting two-position for modulating valves, thus showing promise for engineering applications.

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Cover page of Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions

Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions

(2017)

Inefficient controlling strategies in heating and cooling systems have given rise to a large amount of energy waste and to widespread complaints about the thermal environment in buildings. An intelligentcontrol method based on a support vector machine (SVM) classifier is proposed in this paper. Skin temperatures are the only inputs to the model and have shown attractive prediction power in recognizingsteady state thermal demands. Data were accumulated from two studies to consider potential use for either individuals or a group of occupants. Using a single skin temperature correctly predicts 80% ofthermal demands. Using combined skin temperatures from different body segments can improve the model to over 90% accuracy. Results show that three skin locations contained enough information forclassification and more would cause the curse of dimensionality. Models using different skin temperatures were compared. Optimal parameters for each model were provided using grid search technique. Considering the overfitting possibility and the cases without learning processes, SVM classifiers with a linear kernel are preferred over Gaussian kernel ones.

Cover page of Building operating systems services: An architecture for programmable buildings.

Building operating systems services: An architecture for programmable buildings.

(2014)

Commercial buildings use 73% of all electricity consumed in the United States [30], and numerous studies suggest that there is a significant unrealized opportunity for savings [69, 72, 81]. One of the many reasons this problem persists in the face of financial incentives is that owners and operators have very poor visibility into the operation of their buildings. Making changes to operations often requires expensive consultants, and the technological capacity for change is unnecessarily limited. Our thesis is that some of these issues are not simply failures of incentives and organization but failures of technology and imagination: with a better software framework, many aspects of building operation would be improved by innovative software applications.

To evaluate this hypothesis, we develop an architecture for implementing building applications in a flexible and portable way, called the Building Operating System Services. BOSS allows software to reliability and portably collect, process, and act on the large volumes of data present in a large building. The minimal elements of this architecture are hardware abstraction, data management and processing, and control design; in this thesis we present a detailed design study for each of these components and consider various tradeoffs and findings. Unlike previous systems, we directly tackle the challenges of opening the building control stack at each level, providing interfaces for programming and extensibility while considering properties like scale and fault-tolerance.

Our contributions consist of a principled factoring of functionality onto an architecture which permits the type of application we are interested in, and the implementation and evaluation of the three key components. This work has included significant real-world experience, collecting over 45,000 streams of data from a large variety of instrumentation sources in multiple buildings, and taking direct control of several test buildings for a period of time. We evaluate our approach using focused benchmarks and case studies on individual architectural components, and holistically by looking at applications built using the framework.

Cover page of Evaluating a Social Media Application for Conserving Energy and Improving Operations in Commercial Buildings

Evaluating a Social Media Application for Conserving Energy and Improving Operations in Commercial Buildings

(2012)

Compared to the wealth of studies on residential energy behavior, studies on the energy attitudes and behaviors of commercial building occupants have been few. However, occupants exert significant control and influence over energy use in commercial buildings, and it has been estimated that 20% to 50% of total building energy use is controlled or impacted by occupants. This study explores the potential for using a web-based social network to promote energy awareness and influence energy-conserving behavior in the workplace. The research team developed a social media application prototype and conducted usability testing with 128 subjects to understand the perspectives of typical office building occupants. The key findings presented are: 1) the influence of personalized energy information; (2) the influence of normative energy information; (3) the potential for sharing personal energy goals and energy data; (4) the effects of incentives such as self-selected goals or rewards, and (5) the implications of using social media for improving communications between building occupants and operators.Findings suggest that highly individualized energy information, at the level or individual workstations or offices, offers benefits for engaging and informing individuals about their energy use, and that the cost of energy is viewed as the most useful energy metric, a finding supported by previous research. Social aspects of sharing energy use information and personal energy goals were also viewed favorably by the usability test participants. Overall the study found considerable potential for using social media to engage commercial building occupants in energy conservation, and to improve communications between occupants and building management. The paper concludes with recommendations for the design of energy feedback systems including those with social media characteristics.