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Cover page of Quantifying Office Building HVAC Marginal Operating Carbon Emissions and Load Shift Potential: A Case Study in California

Quantifying Office Building HVAC Marginal Operating Carbon Emissions and Load Shift Potential: A Case Study in California

(2025)

The operational carbon emissions intensity of the electricity used in a building is commonly treated as a fixed value but grid carbon emissions factors have temporal and geographical variations, which makes building operating emissions dependent on when and where electricity is used. Grid electricity carbon characteristics can be quantified by either average or marginal emission rates, leading to an increasing debate about which metric provides more accurate results for determining the effect of various decarbonization strategies. We advocate for the use of the marginal operating emissions rate (MOER) to evaluate the impacts of demand-side management because it considers the generating plants' dispatch order and is able to reflect the change in emissions induced by demand management. In this study, we examined the benefits of emission-based load-shifting strategies by first analyzing the annual temporal variations of the Northern California grid region and developed a virtual chiller load shift strategy similar to demand response but interacting with the grid MOER signal. We then assessed its effect on the case study building by calculating the avoided emissions on a seasonal and annual basis through a numerical simulation. As a result, we found that for the Northern California region, shifting load is most effective during the spring season with 18% avoided carbon emissions when the grid has more renewable supply. However, the simulated annual result shows 2% avoided carbon emissions indicating the seasonal characteristics of the proposed strategy and the limitation of considering load shift strategy as the single solution to decarbonize.

Cover page of Toward Design Automation for Building Models

Toward Design Automation for Building Models

(2023)

Building performance simulation is an important tool in building design and operations. Its purpose is to evaluate and optimize energy use, environmental impact, and occupant comfort of buildings. However, the current state of building performance simulation tools is highly fragmented, and the models themselves can be of low quality. In this paper, we present a platform-based design paradigm for building performance models. This approach offers a standardized design flow to ensure that the models are developed in a consistent and systematic way. Addition- ally, our approach addresses the lack of model performance metrics, allowing for the quantification of model performance. We explore the design flow and model performance quantification with a case study, demonstrating the use of the platform-based design paradigm.

Cover page of Robo-Chargers: Optimal Operation and Planning ofa Robotic Charging System to Alleviate Overstay

Robo-Chargers: Optimal Operation and Planning ofa Robotic Charging System to Alleviate Overstay

(2023)

Charging infrastructure availability is a major concern for plug-in electric vehicle users. Nowadays, the limited public chargers are commonly occupied by vehicles which have already been fully charged. Such phenomenon, known as overstay, hinders other vehicles’ accessibility to charging resources. In this paper, we analyze a charging facility innovation to tackle the challenge of overstay, leveraging the idea of Robo-chargers -automated chargers that can rotate in a charging station and proactively plug or unplug plug-in electric vehicles. We formalize an operation model for stations incorporating Fixed-chargers and Robo-chargers. Optimal scheduling can be solved with the recognition of the combinatorial nature of vehicle-charger assignments, charging dynamics, and customer waiting behaviors. Then, with operation model nested, we develop a planning model to guide economical investment on both types of chargers so that the total cost of ownership is minimized. In the planning phase, it further considers charging demand variances and service capacity requirements. In this paper, we provide systematic techno-economical methods to evaluate if introducing Robo-chargers is beneficial given a specific application scenario. Comprehensive sensitivity analysis based on real-world data highlights the advantages of Robo-chargers, especially in a scenario where overstay is severe. Validations also suggest the tractability of operation model and robustness of planning results for real-time application under reasonable model mismatches, uncertainties and disturbances.

Cover page of Field Study of Thermal Infrared Sensing for Office Temperature Control

Field Study of Thermal Infrared Sensing for Office Temperature Control

(2023)

The purpose of this paper is to evaluate the performance of a novel office temperature control system. To make occupants more comfortable with less energy, we have been developing a new system that uses an inexpensive infrared camera to evaluate occupants’ thermal sensation and optimize room temperature. The system (1) detects the positions of a person’s face, nose, and hands in a thermal image taken by an infrared camera and measures temperatures in those areas; (2) predicts thermal sensation using measured skin temperatures; and (3) adjusts an HVAC set-point temperature based on the predicted sensation to optimize occupant thermal comfort. We compared the comfort and energy performance of the new system to conventional control using a fixed setpoint of 72.0 °F (22.2 °C) in a small conference room. The results indicate that the conventional control often overcooled the occupants, whereas our system reduced cooling energy consumption and made the occupants more thermally neutral and comfortable than the conventional control.

Cover page of Field Demonstration of the Brick Ontology to Scale up the Deployment of ASHRAE Guideline 36 Control Sequences

Field Demonstration of the Brick Ontology to Scale up the Deployment of ASHRAE Guideline 36 Control Sequences

(2023)

Many commercial buildings have a vast network of sensors as part of their building automation systems (BAS) that allows opportunities for energy consumption and cost savings by deploying advanced control sequences. However, this resource is often underutilized since BAS are typically programmed with simple control sequences with limited potential to deliver on these opportunities. The recent availability of ASHRAE Guideline 36 (G36) with advanced HVAC control sequences supports control retrofits in existing buildings to unlock much of the savings potential. However, barriers such as the lack of standard naming convention of building assets and data points, proprietary equipment and BAS, and the inherent uniqueness of buildings and their systems prevent building stakeholders from adopting any “plug-and-play” implementation of G36. Instead, control vendors must often undertake the manual and labor-intensive point mapping process to identify a data stream’s functional and spatial relationship within the HVAC system along with other relevant contexts and map it to the new control sequences. The vendor must carry out the point mapping process in each individual building since the mapping is unlikely to port over to another building. Even for the same building, the point mapping process can occur multiple times if various control vendors implement different control retrofits and/or multiple control retrofits happen over the lifecycle of the building. Then, there is the likelihood that G36 control sequences are programmed uniquely to the building, preventing the same implementation from being reused in another. Therefore, this paper presents a field demonstration of how we leveraged the Brick ontology with BACnet, OpenBuildingControl’s Control Description Language (CDL), and open-source support tools to implement scalable and portable advanced building controls. These tools provide standardized semantic descriptions and relationships of the building’s assets and data points (Brick), standardized communication protocol to read from and write to the building’s BAS (BACnet), and standardized code implementations (CDL) of standardized advanced control strategies (G36). We implemented G36’s hot water supply temperature setpoint reset in a Berkeley, CA building for this field demonstration. This field demonstration aims to show how integrating these tools may streamline the deployment of advanced control sequences such as G36 in a consistent manner regardless of differences found across buildings.

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.