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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

ComStock Measure Documentation: Variable-Speed Pumps

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - variable speed pumps - and briefly introduces key results. The full public data set can be accessed on the ComStock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Lighting Technology Energy Solutions (LiTES) (Final Technical Report)

The Lighting Technologies Energy Solutions (LiTES) Program Final Technical Report is a collaborative effort between NextEnergy, Consumers Energy, DTE Energy (DTE) and the Detroit Joint Apprentice Training Center (DJATC) to evaluate customer and contractor experiences deploying advanced/networked lighting controls systems (ALC/NLC). The LiTES Program, funded by the US Department of Energy (DOE), sought to reduce energy use in small and medium commercial buildings (SMCB) by accelerating the adoption of ALC/NLC through contractor training and technology deployment. Leveraging recommendations already outlined by the Design Lights Consortium Commercial Advanced Lighting Controls (DLC CALC) project, NextEnergy led an effort to train contractors, evaluate the experience of ALC/NLC demonstration projects, identify opportunities to reduce market barriers, and accelerate the increased adoption of ALC/NLC technologies within SMCB. The LiTES Program defined SMCB as commercial buildings under 100,000 square feet. The LiTES Program efforts also included evaluating current ALC/NLC utility incentives, piloting ALC/NLC incentives specific to SMCB, and identifying opportunities to better align utility incentives with current ALC/NLC technology to support accelerating the adoption of ALC/NLC in SMCB. This report shares the survey results and M&V data findings, including energy savings, market barriers, experience of installations, impact of incentives/financing programs and provides recommendations for the increased deployment of ALC/NLC systems in small and medium commercial buildings. Utility survey results, incentive alternatives and recommendations, including a cost benefit analysis outlining energy and economic impacts of the provided ALC/NLC incentives on SMCB projects are also provided.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

ComStock Reference Documentation (V.1)

The commercial building sector stock model, or ComStock™, is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. ComStock asks and answers two questions: how is energy used in the U.S. building stock and what are the impact of energy saving technologies. Specifically, ComStock identifies where energy is being consumed geographically, in what building types and end uses, and at what times of day. Simultaneously, it identifies the impact of efficiency measures: how much energy do efficiency measures save; where, or in what use cases do measures save energy; when, or at what time of day do savings occur; and which building stock segments have the biggest savings potential. This document contains the methodology and assumptions behind ComStock and serves as a guide to its use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Retrofitting Buildings with Solar-Reflective Roofs and Walls and its Impact on Peak Power Demand

Buildings are a major consumer of electricity in the United States and a significant portion of the consumption comes from heating, ventilation, and air-conditioning (HVAC) applications. Passive cooling strategies in the building envelope help to reduce the energy consumption for HVAC as well as peak electricity demand. Although being one of the most cost-effective passive cooling strategies, modern reflective roofing and reflective exterior wall technology is not well documented for its impact on peak demand. This study utilized whole building energy simulations on residential and commercial building prototype models to quantify the impact of cool roofs and cool exterior walls. The analysis was performed in three climate zones with varying insulation levels and solar reflectances for roofs and exterior walls. For both the residential and commercial buildings, the baseline building had a roof solar reflectance value of 0.10 and an exterior wall solar reflectance value of 0.25. The results from the simulations show that roofs and exterior walls with higher reflectance values increase cooling energy savings but can also increase heating energy consumption. The impact of changes in solar reflectances was greater in buildings with low roof/wall insulation levels compared to roofs/walls with higher insulation levels. A baseline for the simulations was set with the roof and exterior wall solar reflectances set at 0.1 and 0.25, respectively and simulations having varying roof and exterior wall thermal resistances were compared to the baselines.

14 SOLAR ENERGY↗
DOE OSTI2022

Prototype Courthouse Building Energy Model: Building Characteristics and Energy Simulation Results

To coordinate commercial building energy research activities, the US Department of Energy uses a suite of commercial prototype building models, which includes 16 building types and covers 80% of US commercial floorspace. Efforts are underway to expand this suite by developing prototype models for additional building types, including a courthouse. Informed by building design guides, databases, documented projects, and personal communication with courthouse design experts, a 3-story, 4-courtroom, 69,324 ft 2 building was considered as the prototype courthouse to represent an average-sized courthouse in the United States. This report documents the building and system characteristics of the prototype courthouse model combined with climate-specific and construction vintage–specific requirements for the building envelope and systems from ASHRAE Standard 90.1. The energy simulation results are presented for 4 construction vintages (2004, 2007, 2010, and 2013) and 15 ASHRAE climate zones. To verify the energy simulation results, energy use statistics of existing courthouses from different building database are also presented. Based on the simulated energy use, the site energy use intensity of the prototype courthouse ranged from 45 to 159 kBtu/ft 2 compared with the average energy use intensity of 94.7 kBtu/ft 2 for the courthouse building type in the 2012 Commercial Building Energy Consumption Survey (CBECS) Data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Testing and validating SMDS algorithms implemented in the cloud

Pacific Northwest National Laboratory (PNNL) provided technical assistance to NorthWrite Inc. under the Small Business Vouchers (SBV) Pilot. NorthWrite delivered services to owners of small commercial buildings, using a cloud-based service to monitor, control, and optimize building operations, saving energy and reducing operating costs for the owners while ensuring that occupant comfort needs were consistently met. NorthWrite had a longstanding desire to add a new suite of diagnostic capabilities to their service offering and had been trying, without success, to incorporate several diagnostic algorithms published by PNNL. These algorithms arise from research previously supported by BTO. Through the SBV awarded to NorthWrite, PNNL made available technical knowledge regarding the derivation and application of the following sets of algorithms for use in the NorthWrite Cloud-based service delivery system: • algorithms for monitoring rooftop packaged air conditioners and heat pumps (often referred to as rooftop units or RTUs) and diagnosing faults in these units, • advanced algorithms for automated fault detection and diagnosis of other equipment found in small buildings, and • algorithms for identifying, prioritizing, and assessing the economic effectiveness of implementing energy saving measures in small commercial buildings based on sensed data. The PNNL researchers involved were the original developers of these algorithms, had a unique understanding of the derivation of the algorithms, and had the source data used for this derivation. Further, the PNNL researchers had extensive experience using these algorithms in the laboratory, unique experience applying these algorithms in real-world small commercial buildings and could solve a number of key problems that NorthWrite and other users faced in using these algorithms at scale. In addition, PNNL had recently installed a pair of RTUs in a laboratory setting that were instrumented and connected with data acquisition systems that provided a unique test rig for validating the algorithms before NorthWrite began to deploy their new services in the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Implementation and validation of optimal start control strategy for air conditioners and heat pumps

Commercial buildings are responsible for approximately 20 % of the total energy consumption and greenhouse gas emissions in the United States. Over 85 % of these buildings lack building automation systems, and many are small (<50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, several operational deficiencies lead to excess energy consumption. Studies have shown that managing the RTUs’ heating and cooling set points, schedules, setbacks, and optimal start can result in a 20 % to 25 % reduction in electricity consumption in small commercial buildings. These buildings typically use fixed schedules to start the RTUs 60 to 120 min before occupancy begins, which results in excess energy consumption. This paper presents research that demonstrates and evaluates the performance of four optimal start methods, which utilize data-based modeling as a key element in facilitating adaptive control in response to time-varying inputs while requiring minimal sensor inputs. The evaluation found energy savings in two commercial buildings equipped with RTUs by periodically alternating four different optimal start models during the cooling and heating season. The resulting energy savings are positive for all models and range from 2 to 5 kWh/day/unit. The units on the east side of the building showed higher savings, while interior units showed greater variability in savings due to the differences in capacities and room sizes. Savings were considerably greater during the heating season compared to the cooling season. The performance of all four models on Mondays was poor; models suggested a shorter optimal start time, which resulted in relatively larger errors. Finally, the future work will look at using a different model for the days after weekends and holidays.

42 ENGINEERING↗
DOE OSTI2020

Creating a Resilience Risk Management Plan

This fact sheet provides actionable planning steps for developing a resilience roadmap in your organization. These steps can serve as an outline to help portfolio managers and other stakeholders manage the financial performance of a commercial building portfolio.

Resilience, Resiliency, Commercial building, comme↗
DOE OSTI2026

ComStock Measure Documentation: Fan Static Pressure Reset for Multizone Variable Air Volume Systems

This report assesses the potential for nationwide adoption of a duct static pressure reset in MZ VAV systems in appropriate applications. Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models, this work produces national datasets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The “baseline” model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass-adoption impact on the baseline building stock. “Measures” refers to various “what-if” scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public datasets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario—Fan Static Pressure Reset for Multizone Variable Air Volume (VAV) Systems—and briefly introduces key results. The full public dataset can be accessed on the ComStock data lake or via the Data Viewer at comstock.nrel.gov. The public dataset enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ComStock Measure Documentation: Thermostat Setbacks During Unoccupied Periods

This report assesses the potential for nationwide adoption of thermostat setbacks in appropriate applications. Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models, this work produces national datasets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The “baseline” model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass-adoption impact on the baseline building stock. “Measures” refers to various “what-if” scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public datasets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario— Thermostat Setbacks During Unoccupied Periods—and briefly introduces key results. The full public dataset can be accessed on the ComStock data lake or via the Data Viewer at comstock.nrel.gov. The public dataset enables users to create custom aggregations of results for their use cases (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

ComStock Measure Scenario Documentation: Standard Performance Heat Pump Rooftop Unit With New Windows

Building on a 3-year effort to calibrate and validate the U.S. Department of Energy's ResStock (TM) and ComStock (TM) models, this work produces national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual energy consumption (at subhourly resolution) of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. The goal of this work is to develop energy efficiency and demand flexibility end-use load shapes that cover high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to various "what-if" scenarios that can be applied to buildings. An end-use savings shape is the difference in energy consumption between a baseline building (or collection of buildings) and a building with an energy efficiency or demand flexibility measure applied. It results in a time-series profile broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step, as well as annual aggregations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

ComStock Measure Scenario Documentation: Chiller Replacement

Building on a 3-year effort to calibrate and validate the U.S. Department of Energy's ResStock (TM) and ComStock (TM) models, this work produces national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual energy consumption (at a subhourly resolution) of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. The goal of this work is to develop energy efficiency and demand flexibility end-use load shapes that cover high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to various "what-if" scenarios that can be applied to buildings. An end-use savings shape is the difference in energy consumption between a baseline building (or collection of buildings) and a building with an energy efficiency or demand flexibility measure applied. It results in a time-series profile broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step, as well as annual aggregations. This report describes the modeling methodology for a single end-use savings shape measure - chiller replacement - and briefly introduces key results. The full public dataset can be accessed on the ComStock (TM) data lake or via the Data Viewer at comstock.nrel.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Assessing and Reducing Plug and Process Loads in Office Buildings

Plug and process loads (PPLs) account for 33% of U.S. commercial building electricity consumption (McKenney et al. 2010). (See Figure 1.) Minimizing these loads is a significant challenge in the design and operation of an energy-efficient building. Lobato et al. (2011) and Lobato et al. (2012) define PPLs as energy loads that are not related to general lighting, heating, ventilation, cooling, and water heating, and that typically do not provide comfort to the occupants. The percentage of total building energy use from PPLs is increasing. According to the U.S. Department of Energy (DOE), by 2030, commercial building energy consumption is expected to increase by 24%; PPL energy consumption is anticipated to increase by 49% in the same time frame (DOE 2010). These trends illustrate the importance of PPL energy reduction to achieve an overall goal of reducing whole-building energy consumption.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Assessing and Reducing Plug and Process Loads in Retail Buildings

Plug and process loads (PPLs) account for 33% of U.S. commercial building electricity consumption (McKenney et al. 2010). (See Figure 1.) Minimizing these loads is a significant challenge in the design and operation of an energy-efficient building. Lobato et al. (2011) and Lobato et al. (2012) define PPLs as energy loads that are not related to general lighting, heating, ventilation, cooling, and water heating, and that typically do not provide comfort to the occupants. The percentage of total building energy use from PPLs is increasing. According to the U.S. Department of Energy (DOE), by 2030, commercial building energy consumption is expected to increase by 24%; PPL energy consumption is anticipated to increase by 49% in the same time frame (DOE 2010). These trends illustrate the importance of PPL energy reduction to achieve an overall goal of reducing whole-building energy consumption.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Decarbonizing the U.S. Economy by 2050: A National Blueprint for the Buildings Sector

Residential and commercial buildings are among the largest sources of carbon dioxide and other greenhouse gas (GHG) emissions in the United States, responsible for more than one-third of total U.S. GHG emissions. There are nearly 130 million existing buildings in the United States, with 40 million new homes and 60 billion square feet of commercial floorspace expected to be constructed between now and 2050. Today, most buildings consume large amounts of energy and cause significant climate pollution to meet our basic needs. Buildings account for 74% of U.S. electricity use and building heating and cooling drives peak electricity demand. Moreover, buildings are where electric vehicles (EVs), solar, storage, heat pumps, water heaters, and other distributed energy resources integrate with the electricity system. Consequently, the buildings sector will play a key role in achieving economywide net-zero emissions by 2050.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Automatic Receptacle Controls: Evaluating the clarity of receptacle markings

Plug loads and the electric loads of devices plugged into receptacles in commercial buildings play a significant and growing role in commercial buildings. Because plug loads are portable and are often placed in the building by the occupants, they are a challenging load to manage. This report summarizes an analysis of commercially available automatic receptacle controls (ARCs). As a part of this study, generic versions of ARCs were presented to a general user audience via an online survey, wherein survey respondents indicated which markings more clearly indicated that the receptacle was controlled. A total of 210 responses were collected and included in the analysis presented in this report. The results showed that receptacles with high-contrast markings were consistently clearer and more evident to the users than low-contrast markings. The survey responses also revealed that receptacles with a border drawn around each controlled receptacle were clearer and distinct to the users compared to other markings. These results led to the conclusion that for energy saving technology to be effective, consistent and easy to understand markings are necessary.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

The U.S. power grid is transforming to become smarter, cleaner, and more effi- cient. This is leading to the addition of significant distributed variable renew- able generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed which balance energy generation and consumption with market activity and in- frastructural limitations. Transactive control requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical gray- and black-box models have been widely used to express flexibility, and although these approaches are generally easy to construct and simple to use, they do not capture the non-linear behavior that some end-use loads represent . Machine learning approaches have been proposed to address this limitation. Although deep learning approaches for forecasting end-use loads have been explored, certain aspects of the application of deep models to load forecasting are not well understood. These aspects include how much training data is required, and how models should be structured and trained. To that end, this work explores how to approach applying deep recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We identify data requirements for training accurate models of whole building electricity use conditioned on outdoor temperature, provide insight into model hyperparameter sensitivity, and demonstrate how readily models can be generalized to unseen buildings.

Skomski, Elliott↗
DOE OSTI2021

Connected Loads – Grid Connected Appliances: Commercial Refrigeration System Fault Detection and Diagnostics

As one of the most energy-intensive end-uses in the commercial buildings sector, supermarkets consume around 50 kWh/ft 2 (or 537.6 kWh/m 2 ) of electricity annually, or more than 2 million kWh of electricity per year for a typical store. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40–60% of its total electricity usage and is equivalent to about 2–3% of the total energy consumed by commercial buildings in United States, or around 0.5 quadrillion Btu (or 0.53 quadrillion KJ). Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Current supermarket refrigeration systems rely on high global warming potential hydrofluorocarbon refrigerants. Reducing refrigerant usage or using environment friendly alternatives can result in significant climate benefits. Transcritical CO2 refrigeration systems have attracted more attention in recent years because of their zero-carbon emission advantages compared with traditional refrigerants. These systems are widely used in commercial buildings such as supermarkets. The refrigeration system can also be adapted to handle flexible building loads and be integrated into grid response transactive control to balance the supply and demand of the electric grid. Even minor improvements in the efficiency and operational reliability of supermarket refrigeration systems can create significant value in terms of saving energy, improving food quality, protecting the environment, reducing carbon footprint, and enhancing electric grid resilience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗