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.
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.
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.
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.
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.
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.
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.
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.