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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Solar+Storage for Household Back-up Power: Implications of building efficiency, load flexibility, and electrification for backup during long-duration power interruptions [Slides]

The study analyzes the evolving role of solar+storage for home backup power during long-duration power interruptions. In particular, it evaluates how required storage sizing is impacted as homes become more efficient, flexible, and electrified. The study relies on NREL’s ResStock building modeling platform to create statistically representative distributions of the existing building stock in ten locations across the United States. It then shows how the amount battery storage required for backup power rises or falls as a series of building envelope efficiency, load flexibility, and electrification measures are applied across the building stock in each region. The study also includes sensitivities to show how backup power requirements are impacted by the timing and duration of power interruptions, and explores variation in backup power requirements across the building stock within each study location. The results demonstrate the value of pairing solar+storage with efficiency upgrades, smart home controls, and (in mild winter climates) efficient heat pump retrofits. That value comes in the form of reducing the amount of storage required and/or extending the range of interruption conditions over which a given system can provide backup power (i.e., more extreme weather and/or longer interruptions). Heat pumps in cold-weather climates can pose a challenge for solar+storage backup power, given the amount of storage required, though are a vast improvement over electric-resistance heating. Retaining existing fossil-based heating systems for occasional use during power interruptions, as either the primary or supplementary source of heat, can mitigate this challenge. Other forms of building electrification (e.g., cooking and water heating) generally have marginal impacts on backup battery sizing, given their relatively small energy demand.

14 SOLAR ENERGY↗
DOE OSTI2023

Learning to Predict and Improve Build Successes in Package Ecosystems

Software has become increasingly complex, with a typical application depending on tens or hundreds of packages. Finding compatible versions and build configurations of these packages is challenging. This paper presents a method to learn the likelihood of software build success, and techniques for leveraging this information to guide dependency solvers to better software configurations. We leverage the heavily parameterized package recipes from the Spack package manager to produce a training data set of builds, and we use Graph Neural Networks to learn whether a given package configuration will build successfully or not. We apply our tool to the U.S. Exascale Computing Project’s software stack. We demonstrate its effectiveness in predicting whether a given package will build successfully. We show that our technique can be used to improve the solutions generated by dependency solvers, reducing the need for developers to find working builds by trial and error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

San Xavier District Solar Energy Project for Administration Building and Education Center

The San Xavier District of the Tohono O’odham Nation (District) developed grid-tied solar photovoltaic (PV) systems for two tribal buildings: the San Xavier District Administration Building and Education Center. For the Administration Building, an approximately 182-kilowatt (kW) solar PV system is projected to produce roughly 310,000 kilowatt-hours (kWh) per year and displace about 80% of the energy use of the building. For the Education Center, an approximately 73.5-kw system is projected to produce roughly 130,000 kWh per year and displace about 95% of the building’s energy use. The anticipated 25-year cost savings from the combined 255-kW systems are estimated to be approximately $1,750,000 and $820,000 for the Administration Building and the Education Center, respectively.

14 SOLAR ENERGY↗
DOE OSTI2024

Design And Control of Thermal Storage for Ventilative Cooling in Multifamily Buildings (Final Report)

The concept of natural ventilation is to provide a heat sink for cooling the building during the occupancy periods at night and improve indoor thermal comfort during the daytime without or with minimal need for mechanical cooling. Commonly, natural ventilation through the window or mechanical circulation of the air through the ventilation ducts are two key methods used to achieve night ventilation. Due to the increasing electric need for space heating and cooling, renewable energies combined with energy storage systems are receiving attention worldwide nowadays. When designed and controlled properly, this strategy can reduce energy use for cooling, reduce the size of mechanical cooling equipment, reduce peak electrical demand for cooling, and better enable demand flexibility for mechanical cooling equipment. In some cases, ventilative cooling can eliminate the need for mechanical cooling altogether. For these reasons, California’s Building Energy Efficiency Standards have recently added prescriptive requirements that all new single-family residences (in most California climates) must include ventilative cooling systems (aka: “whole house fans”). However, the current standards do not address multifamily buildings because market-available ventilative cooling products are designed for single-family residences and are physically incompatible with many multifamily building archetypes. The results from the study show that the combination of thermal energy storage on building walls along with nighttime ventilation cooling assisted with nighttime ventilation can save total building energy usage by up to 9% and peak load by 16% in moderate climate zones. The controlled nighttime ventilation cooling (activate ventilation only when outdoor conditions are favorable) can also provide total electricity savings of up to 5% and 7% in hot and cold climates, respectively. In the case of moderate climates, kitchen exhaust-assisted nighttime ventilation cooling can eliminate the need for cooling during overnight hours. This can also significantly assist in reducing carbon emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Methodology to Establish Performance Targets for Building Energy Codes

This report explains the underlying development of the U.S. NZE building sector performance targets reported in Federal Register Notice XX. This document also includes NZE performance targets for U.S. states, which were developed following the same procedures used to develop the national values. These national and state targets are intended to support the establishment and attainment of NZE goals over time. Where residential and commercial building energy code compliance mechanisms apply to new buildings and major renovations and dictate performance requirements, these targets reflect the recommended, minimum levels of performance. Appendix A provides the performance values and targets by U.S. state for residential and commercial buildings. These include each state’s adopted energy code performance, current published MEC performance value, and a nominal NZE code compliance performance target. The current code and NZE values can be compared to the state adopted code value to indicate the level of advancement needed to move towards recommended NZE building performance levels. Additional supplemental resources are available to support states and local governments ready to move from setting NZE policy goals to adopting NZE codes. More information about these resources and the actions states can take to achieve NZE buildings with energy codes are provided in the body of this document.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach

High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

14 SOLAR ENERGY↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2023

Neutrino Flavor Model Building and the Origins of Flavor and CP Violation

The neutrino sector offers one of the most sensitive probes of new physics beyond the Standard Model of Particle Physics (SM). The mechanism of neutrino mass generation is still unknown. The observed suppression of neutrino masses hints at a large scale, conceivably of the order of the scale of a rand unified theory (GUT), which is a unique feature of neutrinos that is not shared by the charged fermions. The origin of neutrino masses and mixing is part of the outstanding puzzle of fermion masses and mixings, which is not explained ab initio in the SM. Flavor model building for both quark and lepton sectors is important in order to gain a better understanding of the origin of the structure of mass hierarchy and flavor mixing, which constitute the dominant fraction of the SM parameters. Recent activities in neutrino flavor model building based on non-Abelian discrete flavor symmetries and modular flavor symmetries have been shown to be a promising direction to explore. The emerging models provide a framework that has a significantly reduced number of undetermined parameters in the flavor sector. In addition, such a framework affords a novel origin of CP violation from group theory due to the intimate connection between physical CP transformation and group theoretical properties of non-Abelian discrete groups. Model building based on non-Abelian discrete flavor symmetries and their modular variants enables the particle physics community to interpret the current and anticipated upcoming data from neutrino experiments. Non-Abelian discrete flavor symmetries and their modular variants can result from compactification of a higher-dimensional theory. Pursuit of flavor model building based on such frameworks thus also provides the connection to possible UV completions: in particular, to string theory. We emphasize the importance of constructing models in which the uncertainties of theoretical predictions are smaller than, or at most compatible with, the error bars of measurements in neutrino experiments. While there exist proof-of-principle versions of bottom-up models in which the theoretical uncertainties are under control, it is remarkable that the key ingredients of such constructions were discovered first in top-down model building. We outline how a successful unification of bottom-up and top-down ideas and techniques may guide us towards a new era of precision flavor model building in which future experimental results can give us crucial insights into the UV completion of the SM.

Almumin, Yahya (ORCID:0000000196396772)↗
DOE OSTI2025

Leveraging Open-Source Satellite-Derived Building Footprints for Height Inference

At a global scale, cities are growing and characterizing the built environment is essential for deeper understanding of human population patterns, urban development, energy usage, climate change impacts, among others. Buildings are a key component of the built environment and significant progress has been made in recent years to scale building footprint extractions from satellite datum and other remotely sensed products. Billions of building footprints have recently been released by companies such as Microsoft and Google at a global scale. However, research has shown that depending on the methods leveraged to produce a footprint dataset, discrepancies can arise in both the number and shape of footprints produced. Therefore, each footprint dataset should be examined and used on a case-by-case study. In this work, we find through two experiments on Oak Ridge National Laboratory and Microsoft footprints within the same geographic extent that our approach of inferring height from footprint morphology features is source agnostic. Regardless of the differences associated with the methods used to produce a building footprint dataset, our approach of inferring height was able to overcome these discrepancies between the products and generalize, as evidenced by 98% of our results being within 3m of the ground-truthed height. This signifies that our approach can be applied to the billions of open-source footprints which are freely available to infer height, a key building metric. This work impacts the broader domain of urban science in which building height is a key, and limiting factor.

Stipek, Clinton [ORNL] (ORCID:0000000280501096)↗
DOE OSTI2016

Impact of buildings on surface solar radiation over urban Beijing

The rugged surface of an urban area due to varying buildings can interact with solar beams and affect both the magnitude and spatiotemporal distribution of surface solar fluxes. Here we systematically examine the impact of buildings on downward surface solar fluxes over urban Beijing by using a 3-D radiation parameterization that accounts for 3-D building structures vs. the conventional plane-parallel scheme. We find that the resulting downward surface solar flux deviations between the 3-D and the plane-parallel schemes are generally ±1–10 W m -2 at 800 m grid resolution and within ±1 W m -2 at 4 km resolution. Pairs of positive–negative flux deviations on different sides of buildings are resolved at 800 m resolution, while they offset each other at 4 km resolution. Flux deviations from the unobstructed horizontal surface at 4 km resolution are positive around noon but negative in the early morning and late afternoon. The corresponding deviations at 800 m resolution, in contrast, show diurnal variations that are strongly dependent on the location of the grids relative to the buildings. Both the magnitude and spatiotemporal variations of flux deviations are largely dominated by the direct flux. Furthermore, we find that flux deviations can potentially be an order of magnitude larger by using a finer grid resolution. Atmospheric aerosols can reduce the magnitude of downward surface solar flux deviations by 10–65 %, while the surface albedo generally has a rather moderate impact on flux deviations. The results imply that the effect of buildings on downward surface solar fluxes may not be critically significant in mesoscale atmospheric models with a grid resolution of 4 km or coarser. However, the effect can play a crucial role in meso-urban atmospheric models as well as microscale urban dispersion models with resolutions of 1 m to 1 km.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A multi-scale time-series dataset of anthropogenic heat from buildings in Los Angeles County

The dataset contains hourly Anthropogenic heat (AH) from buildings in Los Angeles County, based on weather data from 2018. The hourly AH is aggregated at three spatial resolutions: 450m x 450m grid, 12km x 12km grid, and census tract. The AH is broken down into three components: building envelope surface convection, heating, ventilation, and air conditioning (HVAC) system heat release, and zone exfiltration and exhaust air heat loss. The dataset is created with the physics-based EnergyPlus building energy models to calculate individual buildings' AH considering WRF-UCM simulated microclimate conditions. Please refer to the paper "A multi-scale time-series dataset of anthropogenic heat from buildings in Los Angeles County" for more information about the data generation workflow and the data validation procedure. The data set contains two folders: the "output_data" folder holds the simulation results (EP_output and EP_output_csv), building metadata (building_metadata.geojson and building_metadata.csv), aggregated heat emission and energy consumption time-series data (hourly_heat_energy), and geographical data (geo_data) associated with the GEOID referenced in heat and energy consumption data. The "input_data" folder contains the raw data used to generate files in the "output_data" folder as well as data sets used in the validation. The code repository (https://github.com/IMMM-SFA/xu_etal_2022_sdata) holds the processing scripts for data curation, validation, and visualization.

Energy↗
DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗
NASA NTRS1978

Building Specifications

The building in the top photo is the new home of the National Permanent Savings Bank in Washington, D.C., designed by Hartman-Cox Architects. Its construction was based on a money-saving method of preparing building specifications which derived from NASA technology developed to obtain quality construction while holding down cost of launch facilities, test centers and other structures. Written technical specifications spell out materials and components to be used on construction projects and identify the quality tests each item must pass. Specifications can have major impact on construction costs. Poorly formulated specifications can lead to unacceptable construction which must be replaced, unnecessarily high materials costs, safety hazards, disputes and often additional costs due to delays and litigation. NASA's Langley Research Center developed a novel approach to providing accurate, uniform, cost-effective specifications which can be readily updated to incorporate new building technologies. Called SPECSINTACT, it is a computerized - system accessible to all NASA centers involved in construction programs. The system contains a comprehensive catalog of master specifications applicable to many types of construction. It enables designers of any structure to call out relevant sections from computer storage and modify them to fit the needs of the project at hand. Architects and engineers can save time by concentrating their efforts on needed modifications rather than developing all specifications from scratch. Successful use of SPECSINTACT has led to a number of spinoff systems. One of the first was MASTERSPEC, developed from NASA's experience by Production Systems for Architects and Engineers, Inc., an organization established by the American Institute of Architects. MASTERSPEC, used in construction of the bank building pictured, follows the same basic format as SPECSINTACT and can be used in either automated or manual modes. The striking appearance of the bank building shows that, while MASTERSPEC saves time and money, its use involves no sacrfice in architectural design freedom. The Naval Engineering Facilities Command employs an automated specifications system based on SPECSINTACT. The Public Buildings Service of the General Services Administration used SPECSINTACT as a starting point in a plan to make its guideline specifications available to architects and engineers on a nationwide computer network. Public Technology, Inc., a NASA Technology Application Team, is working with Production Systems for Architects and Engineers, Inc., to promote widespread use of the system by state and local governments for cost benefits to taxpayers.

Source record↗
NASA NTRS2010

Jake Garn Mission Simulator and Training Facility, Building 5, Historical Documentation

In response to President George W. Bush's announcement in January 2004 that the Space Shuttle Program (SSP) would end in 2010, the National Aeronautics and Space Administration (NASA) completed a nation-wide historical survey and evaluation of NASA-owned facilities and properties (real property assets) at all its Centers and component facilities. The buildings and structures which supported the SSP were inventoried and assessed as per the criteria of eligibility for listing in the National Register of Historic Places (NRHP) in the context of this program. This study was performed in compliance with Section 110 of the National Historic Preservation Act (NHPA) of 1966 (Public Law 89-665), as amended; the National Environmental Policy Act (NEPA) of 1969 (Public Law 91-190); Executive Order (EO) 11593: Protection and Enhancement of the Cultural Environment; EO 13287, Preserve America, and other relevant legislation. As part of this nation-wide study, in September 2006, historical survey and evaluation of NASA-owned and managed facilities at was conducted by NASA's Lyndon B. Johnson Space Center (JSC) in Houston, Texas. The results of this study are presented in a report entitled, "Survey and Evaluation of NASA-owned Historic Facilities and Properties in the Context of the U.S. Space Shuttle Program, Lyndon B. Johnson Space Center, Houston, Texas," prepared in November 2007 by NASA JSC's contractor, Archaeological Consultants, Inc. As a result of this survey, the Jake Gam Mission Simulator and Training Facility (Building 5) was determined eligible for listing in the NRHP, with concurrence by the Texas State Historic Preservation Officer (SHPO). The survey concluded that Building 5 is eligible for the NRHP under Criteria A and C in the context of the U.S. Space Shuttle program (1969-2010). Because it has achieved significance within the past 50 years, Criteria Consideration G applies. At the time of this documentation, Building 5 was still used to support the SSP as an astronaut training facility. This documentation package precedes any undertaking as defined by Section 106 of the NHPA, as amended, and implemented in 36 CFR Part 800, as NASA JSC has decided to proactively pursue efforts to mitigate the potential adverse affects of any future modifications to the facility. It includes a historical summary of the Space Shuttle program; the history of JSC in relation to the SSP; a narrative of the history of Building 5 and how it supported the SSP; and a physical description of the structure. In addition, photographs documenting the construction and historical use of Building 5 in support of the SSP, as well as photographs of the facility documenting the existing conditions, special technological features, and engineering details, are included. A contact sheet printed on archival paper, and an electronic copy of the work product on CD, are also provided.

Slovinac, Trish↗
NASA NTRSDate not supplied

The Additive Manufacturing Moment Measure - A Parallel Computation Technique for Determining Build Variance in the Laser Powder Bed Fusion Process

Additive manufacturing (AM) has unique process attributes that facilitate the creation of optimized, complex, and unique parts for aerospace applications. However, the multi-scale and complicated building process for AM parts can cause unexpected build conditions that result in microstructural variability within the deposited material. The need for richer datasets and computational modeling capabilities to improve AM process reliability is a consequence of this microstructural variability. A computational approach, referred to as the Additive Manufacturing Moment Measure (AM3), addresses this need by leveraging the way AM processes fuse material in precise, incremental steps. During the layering sequence, each previous step contributes to the condition of the current step at the center of the heat source. The precise steps can be selected to systematically create a time-space point field with co-located machine input and in-situ sensor data. The sequential nature of the AM process, coupled with nearest neighbor calculations, allows for a fully parallel computation for part-scale build profile analysis. The AM3 concept introduced here enables part-scale assessment directly from build files and in-situ process monitoring sensors alike. The AM moment measures were calculated for build point fields and compared with co-located in-situ and ex-situ nondestructive evaluation and optical microscopy observations. These comparisons permit a better understanding of how the sequential process actions can affect the quality of a laser powder bed fusion (LPBF) build. Details of the AM3technique will be discussed and compared to measured LPBF part characteristics. The AM3 results indicate a strong potential to advance the qualification process for aerospace applications.

Additive Manufacturing↗
DOE OSTI2014

An actuarial approach to retrofit savings in buildings

An actuarial method has been developed for determining energy savings from retrofits from energy use data for a number of buildings. This method should be contrasted with the traditional method of using pre- and post-retrofit data on the same building. This method supports the U.S. Department of Energy Building Performance Database of real building performance data and related tools that enable engineering and financial practitioners to evaluate retrofits. The actuarial approach derives, from the database, probability density functions (PDFs) for energy savings from retrofits by creating peer groups for the user’s pre post buildings. From the energy use distribution of the two groups, the savings PDF is derived. This provides the basis for engineering analysis as well as financial risk analysis leading to investment decisions. Several technical issues are addressed: The savings PDF is obtained from the pre- and post-PDF through a convolution. Smoothing using kernel density estimation is applied to make the PDF more realistic. The low data density problem can be mitigated through a neighborhood methodology. Correlations between pre and post buildings are addressed to improve the savings PDF. Sample size effects are addressed through the Kolmogorov--Smirnov tests and quantile-quantile plots.

Subbarao, Krishnappa↗
DOE OSTI2015

Scaling Retro-Commissioning to Small Commercial Buildings: A Turnkey Automated Hardware-Software Solution

In the United States, small commercial buildings represent 51% of total floor space of all commercial buildings and consume nearly 3 quadrillion Btu (3.2 quintillion joule) of site energy annually, presenting an enormous opportunity for energy savings. Retro-commissioning (RCx), the process through which professional energy service providers identify and correct operational problems, has proven to be a cost-effective means to achieve median energy savings of 16%. However, retro-commissioning is not typically conducted at scale throughout the commercial stock. Very few small commercial buildings are retro-commissioned because utility expenses are relatively modest, margins are tighter, and capital for improvements is limited. In addition, small buildings do not have in-house staff with the expertise to identify improvement opportunities. In response, a turnkey hardware-software solution was developed to enable cost-effective, monitoring-based RCx of small commercial buildings. This highly tailored solution enables non-commissioning providers to identify energy and comfort problems, as well as associated cost impacts and remedies. It also facilitates scale by offering energy service providers the means to streamline their existing processes and reduce costs by more than half. The turnkey RCx sensor suitcase consists of two primary components: a suitcase of sensors for short-term building data collection that guides users through the process of deploying and retrieving their data and a software application that automates analysis of sensor data, identifies problems and generates recommendations. This paper presents the design and testing of prototype models, including descriptions of the hardware design, analysis algorithms, performance testing, and plans for dissemination.

Lin, Guanjing↗