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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Intelligent Facades for High Performance Green Buildings. Final Technical Report

Intelligent Facades for High Performance Green Buildings: Previous research and development of intelligent facades systems has been limited in their contribution towards national goals for achieving on-site net zero buildings, because this R&D has failed to couple the many qualitative requirements of building envelopes such as the provision of daylighting, access to exterior views, satisfying aesthetic and cultural characteristics, with the quantitative metrics of energy harvesting, storage and redistribution. To achieve energy self-sufficiency from on-site solar resources, building envelopes can and must address this gamut of concerns simultaneously. With this project, we have undertaken a high-performance building- integrated combined-heat and power concentrating photovoltaic system with high temperature thermal capture, storage and transport towards multiple applications (BICPV/T). The critical contribution we are offering with the Integrated Concentrating Solar Façade (ICSF) is conceived to improve daylighting quality for improved health of occupants and mitigate solar heat gain while maximally capturing and transferring on- site solar energy. The ICSF accomplishes this multi-functionality by intercepting only the direct-normal component of solar energy (which is responsible for elevated cooling loads) thereby transforming a previously problematic source of energy into a high- quality resource that can be applied to building demands such as heating, cooling, dehumidification, domestic hot water, and possible further augmentation of electrical generation through organic Rankine cycles. With the ICSF technology, our team is addressing the global challenge in transitioning commercial and residential building stock towards on-site clean energy self-sufficiency, by fully integrating innovative environmental control systems strategies within an intelligent and responsively dynamic building envelope. The advantage of being able to use the entire solar spectrum for active and passive benefits, along with the potential savings of avoiding transmission losses through direct current (DC) transfer to all buildings systems directly from the site of solar conversion, gives the system a compounded economic viability within the commercial and institutional building markets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Integrated Spatial, Spectral, & Temporal Optical Reflectance System for Precision Occupancy & Location Sensing to Improve Building Energy Efficiency

Buildings consume approximately 35% of the electricity used in the U.S. and building owners can significantly reduce this energy use by providing services like heating, electrical power and lighting only when people are present. The ARPAe funded program titled “INTEGRATED SPATIAL, SPECTRAL, & TEMPORAL OPTICAL REFLECTANCE SYSTEM FOR PRECISION OCCUPANCY & LOCATION SENSING TO IMPROVE BUILDING ENERGY EFFICIENCY” demonstrates how a low cost sensor technology developed for measuring distances can be used to count and locate occupants with a high degree of precision with a very low error rates. This platform tells a building control system where occupants are located (but not who they are) so that energy consuming services can be provided only when the services are needed by building occupants. The original proof of concept involved using low cost, commercially available time-of-flight (TOF) sensors that measure distance, but the performance of these existing sensors was lacking, as they could not operate properly in the presence of sunlight, which blinded the simple TOF sensors and limited their utility in buildings. This project proposed a powerful new class of TOF sensors that used state-of-the-art integrated circuit (IC) fabrication processes that combined advanced photonics with conventional silicon chip circuitry for improved sensor performance. An equally important part of this project was to find ways to maximize occupant count and location accuracy while using the fewest number of sensors possible, in order to keep costs low. By using building blueprints to create digital twins of commercial building spaces, the team developed new algorithms to maximize occupant count and tracking accuracy by properly locating the minimum number of sensors at just the right spots in the building. This capability not only minimizes system costs but also simplified sensor installation and system commissioning. Our simulations of our sensor networks for a range of commercial floorplan designs demonstrated that our installed cost target of $0.08/sqft was attainable, though not fully demonstrated during the project. Finally, we noted that the TOF sensor concept could provide a valuable role in health and eldercare by tracking patients without the need for worn sensors and would be useful for fall detection and other patient safety metrics, including tracking healthcare/patient interactions. We feel that, when fully developed, this new class of sophisticated TOF sensors and support software will be a powerful new approach to improving building energy efficiency based on occupant centric control platforms and will also open new levels of patient safety in healthcare operations. To realize this potential, the team formed the Troy Sensor Company LLC to oversee licensing of the programs patents and continue to seek commercialization of this program’s activity sensing technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

AlphaBuilding - Synthetic Buildings Operation Dataset

This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

AlphaBuilding↗
DOE OSTI2011

Using Whole-Building Electric Load Data in Continuous or Retro-Commissioning

Whole-building electric load data can often reveal problems with building equipment or operations. In this paper, we present methods for analyzing 15-minute-interval electric load data. These methods allow building operators, energy managers, and commissioning agents to better understand a building's electricity consumption over time and to compare it to other buildings, helping them to 'ask the right questions' to discover opportunities for electricity waste elimination, energy efficiency, peak load management, and demand response. For example: Does the building use too much energy at night, or on hot days, or in the early evening? Knowing the answer to questions like these can help with retro-commissioning or continuous commissioning. The methods discussed here can also be used to assess how building energy performance varies with time. Comparing electric load before and after fixing equipment or changing operations can help verify that the fixes have the intended effect on energy consumption. Analysis methods discussed in this paper include: ways to graphically represent electric load data; the definition of various parameters that characterize facility electricity loads; and a regression-based electricity load model that accounts for both time of week and outdoor air temperature. The methods are illustrated by applying them to data from commercial buildings. We demonstrate the ability to recognize changes in building operation, and to quantify changes in energy performance. Some key findings are: 1) Plotting time series electric load data is useful for understanding electricity consumption patterns and changes to those patterns, but results may be misleading if data from different time intervals are not weather-normalized. 2) Parameter plots can highlight key features of electric load data and may be easier to interpret than plots of time series data themselves. 3) A time-of-week indicator variable (as compared to time-of-day and day-of-week indicator variables) improves the accuracy of regression models of electric load. 4) A piecewise linear and continuous outdoor air temperature dependence can be derived without the use of a change-point model (which would add complexity to the modeling algorithm) or assumptions about when structural changes occur (which could introduce inaccuracy). 5) A model that includes time-of-week and temperature dependence can be used for weather normalization and can determine whether the building is unusually temperature-sensitive, which can indicate problems with HVAC operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Overview of the Westinghouse Small Modular Reactor building layout

The Westinghouse Small Modular Reactor (SMR) is an 800 MWt (>225 MWe) integral pressurized water reactor (iPWR), in which all of the components typically associated with the nuclear steam supply system (NSSS) of a nuclear power plant are incorporated within a single reactor pressure vessel. This paper is the third in a series of four papers, which describe the design and functionality of the Westinghouse SMR. It focuses in particular upon the plant building layout and modular design of the Westinghouse SMR. In the development of small modular reactors, the building layout is an area where the safety of the plant can be improved by applying new design approaches. This paper will present an overview of the Westinghouse SMR building layout and indicate how the design features improve the safety and robustness of the plant. The Westinghouse SMR is designed with no shared systems between individual reactor units. The main buildings inside the security fence are the nuclear island, the rad-waste building, the annex building, and the turbine building. All safety related equipment is located in the nuclear island, which is a seismic class 1 building. To further enhance the safety and robustness of the design, the reactor, containment, and most of the safety related equipment are located below grade on the nuclear island. This reduces the possibility of severe damage from external threats or natural disasters. Two safety related ultimate heat sink (UHS) water tanks that are used for decay heat removal are located above grade, but are redundant and physically separated as far as possible for improved safety. The reactor and containment vessel are located below grade in the center of the nuclear island. The rad-waste and other radioactive systems are located on the bottom floors to limit the radiation exposure to personnel. The Westinghouse SMR safety trains are completely separated into four unconnected quadrants of the building, with access between quadrants only allowed above grade. This is an improvement to conventional reactor design since it prevents failures of multiple trains during floods or fires and other external events. The main control room is located below grade, with a remote shutdown room in a different quadrant. All defense in depth systems are placed on the nuclear island, primarily above grade, while the safety systems are located on lower floors. The economics of the Westinghouse SMR challenges the established approach of large Light Water Reactors (LWR) that utilized the economies of scale to reach economic competitiveness. To serve the market expectation of smaller capital investment and cost competitive energy, a modular design approach is implemented within the Westinghouse SMR. The Westinghouse SMR building layout integrates the three basic design constraints of modularization; transportation, handling and module-joining technology. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Control Limits for Building Energy End Use Based on Engineering Judgment, Frequency Analysis, and Quantile Regression

Approaches are needed to continuously characterize the energy performance of commercial buildings to allow for (1) timely response to excess energy use by building operators; and (2) building occupants to develop energy awareness and to actively engage in reducing energy use. Energy information systems, often involving graphical dashboards, are gaining popularity in presenting energy performance metrics to occupants and operators in a (near) real-time fashion. Such an energy information system, called Building Agent, has been developed at NREL and incorporates a dashboard for public display. Each building is, by virtue of its purpose, location, and construction, unique. Thus, assessing building energy performance is possible only in a relative sense, as comparison of absolute energy use out of context is not meaningful. In some cases, performance can be judged relative to average performance of comparable buildings. However, in cases of high-performance building designs, such as NREL's Research Support Facility (RSF) discussed in this report, relative performance is meaningful only when compared to historical performance of the facility or to a theoretical maximum performance of the facility as estimated through detailed building energy modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Machine Learning for Automated Metadata Assignment in Buildings: Cooperative Research and Development (Final Report, CRADA Number CRD-18-00767)

RealTerm Energy and NREL have identified a shared vision to evaluate opportunities to facilitate the organization and assignment of metadata to building control system (BCS) data via industry-informed machine learning (ML). Manual metadata assignment is labor intensive and costly, slowing down any Energy Management and Information System (EMIS) deployment in the building space. This project aims to develop methodologies to accurately assign this metadata and significantly decrease the level of effort associated with deploying EMIS. The objective of this project is to identify/design methodologies to assign metadata to HVAC control points automatically. The identified methodologies will be programmed in analytics algorithms so they can ingest a list of points and produce a detailed tagging following the Haystack classification nomenclature. To validate the efficacy of each methodology, tagging results will be compared utilizing a list of points extracted from RealTerm's building database-as well as data extracted from the NREL campus via the Intelligent Campus program-enabling testing against large datasets with real world challenges. The developed methodologies may leverage building manager/operator input on a limited basis to add context to the classifying algorithms. The partnership aims to advance global efforts in areas related to the DOE missions through improving operational performance of commercial buildings. It is well documented that buildings fall out of commission after they are occupied, wasting significant energy and incurring associated costs simply due to poor operational performance. Emerging EMIS technologies that perform continuous commissioning help to address this issue, yet integration of these systems can be labor intensive both for the technology vendor and the building owner/operator. This project will enable more efficient and cost-effective analytics for buildings, enabling improvement in building operations at lower cost points.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Commercial Building Sensors and Controls Systems: Barriers, Drivers, and Costs

Building sensors and controls systems, including building automation systems, consists of the sensor-based devices installed in buildings and the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 8% of small commercial buildings, however, have installed sensors and controls systems. This is largely due to cost barriers. This work seeks to increase the transparency of system costs and identify specific barriers and drivers for increased adoption. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 20 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. The qualitative interview data was analyzed using grounded theory to identify overarching concepts, such as barriers, drivers, and future directions on the field. From this analysis, primary barriers were found to be complexity, a lack of knowledge, and money. Primary drivers were found to be the knowledge of data, savings, and remote access. The qualitative cost data was collected in the form of invoices during the interviews. The cost values were used to develop a percentage-based cost stack which identifies the average fraction of the total cost attributed to each category (hardware, software, labor, fees, and taxes). This greater understanding of costs and barriers associated with commercial building sensors and controls systems lays the groundwork for future steps in increasing system adoption, reducing energy consumption, and market transformation.

advanced building controls↗
DOE OSTI2023

Commercial Building Sensors and Controls Systems: Barriers and Drivers: Preprint

Building sensors and controls systems, including building automation systems, comprise the sensor-based devices installed in buildings as well as the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 13% of small commercial buildings, however, have installed sensors and controls systems, largely because of cost barriers. To accelerate adoption, this work seeks to increase the transparency of system costs and identify specific barriers and drivers. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 21 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. We collected the cost data in the form of invoices and used it to develop a percentage-based cost category breakdown. The interview data were analyzed using grounded theory to identify overarching concepts such as barriers, drivers, and future directions. From this analysis, we found the primary barriers to be complex and confusing systems, lack of user skills, and financial concerns, and the primary drivers to be operational benefits, insight into operations, and remote access to data. The future directions analysis highlighted the potential technological solutions to address gaps and barriers, as well as predicted drivers to increase adoption. This greater understanding of the costs, barriers, and drivers associated with commercial building sensors and controls systems lays the groundwork for increasing system adoption, reducing energy consumption, and transforming the market.

building automation system↗
DOE OSTI2017

Cybersecurity and Optimization in Smart “Autonomous” Buildings

Significant resources have been invested in making buildings “smart” by digitizing, networking and automating key systems and operations. Smart autonomous buildings create new energy efficiency, economic and environmental opportunities. But as buildings become increasingly networked to the Internet, they can also become more vulnerable to various cyber threats. Automated and Internet-connected buildings systems, equipment, controls, and sensors can significantly increase cyber and physical vulnerabilities that threaten the confidentiality, integrity, and availability of critical systems in organizations. Securing smart autonomous buildings presents a national security and economic challenge to the nation. Ignoring this challenge threatens business continuity and the availability of critical infrastructures that are enabled by smart buildings. In this chapter, the authors address challenges and explore new opportunities in securing smart buildings that are enhanced by machine learning, cognitive sensing, artificial intelligence (AI) and smart-energy technologies. The chapter begins by identifying cyber-threats and challenges to smart autonomous buildings. Then it provides recommendations on how AI enabled solutions can help smart buildings and facilities better protect, detect and respond to cyber-physical threats and vulnerabilities. Next, the chapter will provide case studies that examine how combining AI with innovative smart-energy technologies can increase both cybersecurity and energy efficiency savings in buildings. The chapter will conclude by proposing recommendations for future cybersecurity and energy optimization research for examining AI enabled smart-energy technology.

Mylrea, Michael E.↗
DOE OSTI2022

Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings

Building demand flexibility (DF) research has recently gained attention. To unlock building DF as a predictable grid resource, we must establish a quantitative understanding of the resource size, performance variability, and predictability based on large empirical datasets. Researchers have proposed various sets of theoretical metrics to measure this performance. Some metrics have been applied to simulation results, but most fall short of exploring the complexities in real building applications. There are practical metrics used in individual demand response field studies but they alone cannot fulfil the job of DF benchmarking across a diverse group of buildings. The electrical grid's geographically diverse and changing nature presents challenges to comparing building DF performance measured under different conditions (i.e., benchmarking DF). To address this challenge, a novel DF benchmarking framework focused on load shedding and shifting is presented; the foundation is a set of simple, proven single-event metrics with attributes describing event conditions. These enable benchmarking and visualization in different dimensions for identifying trends that represent how these attributes influence DF. To test its feasibility and scalability, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand response participation data. Furthermore, these examples provided a pathway for using both building level benchmarking and aggregation to extract insights into building DF about magnitude, consistency, and influential factors. Potential applications of the framework and real-world values have been identified for grid and building stakeholders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Influence of build direction on the fracture mechanism of 3D printed octet lattices

Here, we investigate the effects of 3D printing build directions on the fracture properties of octet lattice metamaterials made of polylactic acid (PLA) and how these effects vary with the relative density of the lattices. Single-edge notch bend samples are 3D printed in two orthogonal build directions at various relative densities. Our results show that the work of fracture for octet lattices with build directions parallel to the crack plane is significantly higher than those with build directions perpendicular to the crack plane. We also observed that the ratio of specific work of fracture between the two build directions remains nearly constant across different relative densities. In contrast, the ratio of peak load between the two build directions decreases as relative density increases. Furthermore, the build direction dictates the fracture mechanism. While the perpendicular build direction predominantly results in a brittle (mode I) fracture, the parallel build direction leads to a complex, delamination-dominated (mode II type) fracture. This phenomenon is largely governed by the weak interfaces formed between the printed layers and their interaction with the lattice geometry. These results reveal that the build direction governs the fracture mechanism and work of fracture of these lattice metamaterials and is therefore an important design consideration.

3D printed PLA↗
DOE OSTI2017

IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods

One of the most significant barriers to achieving deep building energy efficiency is a lack of knowledge about the factors determining energy use. In fact, there is often a significant discrepancy between designed and real energy use in buildings, which is poorly understood but are believed to have more to do with the role of human behavior than building design. Building energy use is mainly influenced by six factors: climate, building envelope, building services and energy systems, building operation and maintenance, occupants’ activities and behavior, and indoor environmental quality. In the past, much research focused on the first three factors. However, the next three human-related factors can have an influence as significant as the first three. Annex 53 employed an interdisciplinary approach, integrating building science, architectural engineering, computer modeling and simulation, and social and behavioral science to develop and apply methods to analyze and evaluate the real energy use in buildings considering the six influencing factors. Finally, outcomes from Annex 53 improved understanding and strengthen knowledge regarding the robust prediction of total energy use in buildings, enabling reliable quantitative assessment of energy-savings measures, policies, and techniques.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Achieving success with grid-interactive efficient buildings (GEBs) is closely tied to the utilization of flexible loads. A valuable strategy involves the implementation of precooling techniques before high-demand events, such as peak hours, by adjusting zone air temperature setpoints. This leads to a reduction in thermal loads and peak electricity demand during these times, as the building’s thermal mass stores and subsequently releases thermal energy. However, the effectiveness of the pre-cooling optimization is highly contingent on specific conditions such as building thermal properties, weather conditions, utility rate structure, HVAC equipment sizing, etc. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Here, the optimal thermostat schedule was parameterized using three optimization variables: the precooling start time, the precooling end time, and the precooling temperature setpoint. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. In addition, we investigated the impact of different influencing factors on the optimal precooling strategy. These factors include building thermal mass, outdoor air conditions, and energy price profiles. Using high-performance computing, we simulated a total of 225 scenarios, consisting of three levels of thermal mass, five typical outdoor air temperature profiles, and fifteen time-of-use price plans. The results demonstrate that optimal thermostat scheduling could save substantial energy cost in medium-sized office buildings with heavy thermal mass but with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. The study also highlights the need to account for zone diversity and recognize that a one-size-fits-all-zone setpoint schedule may not be suitable for all zones and can lead to unnecessary energy wastage. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. Although cost savings are comparable under certain conditions with the same cooling load, observed deviations in energy penalty indicate potential disparities in the efficiency of the HVAC system during the load-shifting process. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Automated fault detection and diagnosis deployment Internet of Things solution for building energy system

The building sector is the highest energy consumer and ranks first in terms of carbon emissions among all sectors. To address these issues, decarbonization and electrification in the building energy sector are two critical missions of the new US administration. For decarbonization, the new administration has set a target to reduce greenhouse gas emissions by 50–52% by 2030 and targeting a carbon-neutral economy by 2050. For electrification, the goal is to achieve 100% clean electricity by 2035. Such decarbonization and electrification in the building sector require that energy consumption in buildings be reduced significantly. Therefore, the building sector must continuously adopt new technologies to achieve its energy and carbon emission goals. One of the most fundamental technologies is the Internet of Things (IoT). IoT has proven to be an effective solution for the building domain, including building information/energy modeling, smart buildings, etc. Although much progress has been made in the development of IoT-based building energy systems, there is still a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostic (AFDD) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. Here, this study reviews existing IoT solutions developed for building energy–related application and developed a simple but effective AFDD IoT deployment solution, including developing a suitable IoT architecture and conducting easy and scalable deployment by leveraging a common cloud-based IoT service.

42 ENGINEERING↗
DOE OSTI2021

A synthetic building operation dataset

Abstract This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Global Building Occupant Behavior Database

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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗