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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This case study spotlights Washington, D.C.'s successful implementation of DOE’s Unique Building ID (UBID) tool to leverage building-related datasets in support of their sustainability policies.
Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.
A new web application that advances the science of data-driven, remote building energy analysis to increase the speed and scale of retrofits worldwide could play a key role in reducing greenhouse gas (GHG) emissions and meeting the Paris Agreement's targets. Here, the multi-award-winning Building Efficiency Targeting Tool for Energy Retrofits (BETTER) is a public access web application (better.lbl.gov) sponsored by the U.S. Department of Energy (DOE) and developed by Lawrence Berkeley National Laboratory (Berkeley Lab) and Johnson Controls.
The combination of increased electric load growth across all sectors, deferred electrical infrastructure investment, and other factors resulting in variable electric power supply, has created technical challenges to maintaining a resilient and reliable grid. Many federal, regional, and local efforts are in play to modernize the electric grid, including advancing building technologies and distributed energy resources (DERs) that are utilizing smarter controls to become responsive to both occupant and grid needs. This report reviews ten pilot projects demonstrating how groups of buildings combined with behind-the-meter (BTM) DERs such as electric vehicle (EV) charging, battery storage, flexible HVAC and domestic hot water systems, and photovoltaic systems can reliably and cost effectively provide grid services. Each of the ten pilot projects aim to deliver both energy efficiency and demand flexibility (DF) while supporting load growth. The ten demonstration teams are piloting flexible DER packages across diverse communities of residential and commercial buildings to address a variety of regional grid needs. The outcomes of these pilot projects will be used to inform future scaling through utility program development. This paper characterizes the ten teams, showcasing the decision-making process used by each group to develop their packages (Section 2), the grid services they plan to deliver (Section 3), the types of DER packages selected for deployment within building sectors (Section 4) and trends between building sector, DER types, and grid services In order to achieve community scale benefits, the pilot projects must utilize aggregated control mechanisms for coordinating buildings and DERs together. Several types of coordinated control architectures have evolved amongst the teams, influenced by use type, existing market conditions, and integration type. Three coordinated controls architectures have been characterized, highlighting their use cases, benefits, challenges, and tradeoffs in their design. These insights can aid utilities, control vendors, and developers in scaling community-level energy systems (Paul, 2024). Ultimately, the technology packages selected by the ten teams will be coordinated to provide power system services, also known as grid services. Insights from these demonstrations will be useful for grid operators, regulators, aggregators and other stakeholders as they look to deploy demand flexible resources as grid services in the future. The grid services that each team is targeting for demonstration are described in Section 3 and Section 4. Methods for evaluating the grid services have been described in the paper Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER (MacDonald, 2023). To identify technology packages for demonstration, Section 2 shows that project teams used a range of analysis approaches, including building energy modeling, AMI data analysis, cost-benefit frameworks, and utility pilot data. Some teams emphasized technical modeling to quantify grid impacts and demand reduction potential, while others prioritized economic evaluations, stakeholder input, or exploratory pilots to inform deployment decisions. This diversity reflects the need to tailor selection methods to project goals, available data, and organizational context. Section 5 discusses trends between the DER technologies deployed and the grid service provisions from each team. Residential buildings (multifamily and single family) lean towards technologies that enhance energy efficiency (e.g. weatherization upgrades, smart thermostats) and onsite power generation integration (e.g. solar PV). Commercial building demonstrations prioritize technologies that ensure operational reliability (e.g. battery storage) and centralized energy management systems and optimization solutions. Teams that are deploying controllable storage-based technologies are more likely to provide grid services that require a near real-time response. Teams incorporating load shifting technologies like smart thermostats with HEMs are likely to include energy markets participation and customer bill management offerings. Campus demonstrations are adopting diverse sets of DERs to emphasize renewable generation, paired with centralized control. This section also describes technologies that were considered during project planning but ultimately excluded from final deployment. These demonstrations reveal that effective DER package design should be tailored to building type, customer segment, and construction vintage. Multifamily buildings benefit from centralized HVAC upgrades and supervisory controls, while single-family homes are well-suited for individualized technologies like solar, storage, and smart home energy monitors. Commercial and campus settings prioritize EMIS integration and load optimization. New construction enables cost-effective integration of DER-ready infrastructure, whereas retrofits require deployments aligned with owner and tenant value streams. For utility program planners, early coordination with developers and building owners, paired with segmented and modular program offerings, can improve adoption, scalability, and grid impact.
Buildings generate data throughout their lifecycle – about ownership & taxation, usage, zoning, code compliance, energy use, and retrofits. State and local governments collect this data after it flows through growing networks of people and systems. But collecting data is only half the battle; what’s really needed is information – the actionable insights that lead to successful policy outcomes.
Commercial buildings consumed 36% of electricity, or 1.35 trillion kWh, in the United States in 2017, and almost 30% of this energy was wasted. Much of this loss can be attributed to inefficient heating ventilation and air conditioning (HVAC) systems. By improving the operational conditions of HVAC, significant savings can be achieved. However, most buildings and building equipment do not use costly sub-meters to monitor and address performance issues, and on-site auditing can be expensive and insufficient. Alternatively in this study, we propose a data-driven method to identify savings opportunities using only whole building meter data and without setting foot in the building. For this purpose, we introduced two algorithms that virtually quantify the value of a thermostat setpoint setback and HVAC rescheduling. Additionally, we developed novel methods for detecting occupancy patterns and quantifying the baseload of the HVAC operation. Using a clustering algorithm, we identified those buildings for which HVAC savings was significant and further categorized the buildings based on their potential for savings. A population study of over 432 commercial buildings demonstrated a median percentage energy savings of 1.6% from a baseload reduction and 2.1% from HVAC rescheduling. Additionally, results indicate that retail buildings have the highest potential for savings among the building types studied.
Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.
What percent of newly constructed commercial buildings comply with the energy code? How much energy and cost could be saved if the compliance increased? Which code requirements have both low compliance rates and high savings potential? These are the questions U.S. Department of Energy (DOE) is trying to answer through its Commercial Energy Code Field Study. Previous commercial studies have been very limited and did not result in a widely accepted and tested methodology. DOE’s goal is to create a standardized methodology that can produce actionable results at a reasonable study cost, that can be used by state and local governments and utilities and provides valuable information to policy makers. The field study team implemented the pilot methodology, compiling a data set of 230 office and retail buildings in two climate zones. The approach is based on identifying lost savings on a total energy cost basis rather than simply counting the quantity of measures that do not meet code. This report is focused on the analysis of the collected data. The primary goal was to analyze the data collected during the field study and determine the actual energy cost impact of each measure in a non-compliance situation. The energy impact results allowed for ranking the measures to identify which have the highest potential for lost savings. These results combined with the time required to verify each measure will allow future compliance verification to focus on measures that had a large impact on energy use over the life of the building and those that have the greatest savings recovery potential per verification hour.
What percent of newly constructed commercial buildings comply with the energy code? How much energy and cost could be saved if the compliance increased? Which code requirements have both low compliance rates and high savings potential? These are the questions U.S. Department of Energy (DOE) is trying to answer through its Commercial Energy Code Field Study. Previous commercial studies have been very limited and did not result in a widely accepted and tested methodology. DOE’s goal is to create a standardized methodology that can produce actionable results at a reasonable study cost, that can be used by state and local governments and utilities and provides valuable information to policy makers. The field study team implemented the pilot methodology, compiling a data set of 230 office and retail buildings in two climate zones. The approach is based on identifying lost savings on a total energy cost basis rather than simply counting the quantity of measures that do not meet code. This report is focused on the analysis of the collected data. The primary goal was to analyze the data collected during the field study and determine the actual energy cost impact of each measure in a non-compliance situation. The energy impact results allowed for ranking the measures to identify which have the highest potential for lost savings. These results combined with the time required to verify each measure will allow future compliance verification to focus on measures that had a large impact on energy use over the life of the building and those that have the greatest savings recovery potential per verification hour.
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Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.
This paper describes building energy system production and usage monitoring using examples from the new RETScreen Performance Analysis Module, called RETScreen Plus. The module uses daily meteorological (i.e., temperature, humidity, wind and solar, etc.) over a period of time to derive a building system function that is used to monitor building performance. The new module can also be used to target building systems with enhanced technologies. If daily ambient meteorological and solar information are not available, these are obtained over the internet from NASA's near-term data products that provide global meteorological and solar information within 3-6 days of real-time. The accuracy of the NASA data are shown to be excellent for this purpose enabling RETScreen Plus to easily detect changes in the system function and efficiency. This is shown by several examples, one of which is a new building at the NASA Langley Research Center that uses solar panels to provide electrical energy for building energy and excess energy for other uses. The system shows steady performance within the uncertainties of the input data. The other example involves assessing the reduction in energy usage by an apartment building in Sweden before and after an energy efficiency upgrade. In this case, savings up to 16% are shown.
City Buildings, Energy, and Sustainability (CityBES) is a web-based data and computing platform, focusing on energy modeling and analysis of a city's building stock to support district or city-scale building energy efficiency programs. CityBES uses an international open data standard, CityGML, to represent and exchange 3D city models. CityBES employs EnergyPlus to simulate building energy use and savings from energy efficient retrofits. Other CityBES features include energy benchmarking, district heating and cooling system modeling, rooftop PV analysis, building performance visualization, heat resilience modeling, as well as urban scale mapping of microclimate and heat vulnerability at census tract level. Different from other tools, CityBES uses integrated open and standard 3D city building data and models each individual building using EnergyPlus. CityBES can be used by urban planners, city energy managers, building owners, utilities, energy consultants and researchers.
This project aimed to develop a public building simulation data set that may be used to inform building code development and guidelines for building innovation. The data set consists of several common building types and many representative locations across the United States. A parametric design of building properties was developed to create a range of building energy models that represent common building design decisions with a particular focus on fenestration options. The US Department of Energy prototype building energy models were altered according to a parametric building design and simulated using both current weather data and future weather estimates derived from global climate models. The resulting data set allows for pertinent exploration of building design parameters, including fenestration, within different environments across the United States in the broader context of climate change.
To holistically address building sustainability, Argonne National Laboratory has expanded its Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) life-cycle model with a new GREET Building Module. This report documents life-cycle analysis (LCA) methodology and foreground data that Argonne National Laboratory compiles and develops to address embodied greenhouse gas (GHG) emissions and energy impacts of a wide range of envelope and structural building materials for new construction and retrofits. The methodology and data form the backbone of the GREET Building Module. This research effort focuses on developing consistent LCA methodology that conforms to building LCA standards such as the EN 15978 to address embodied GHG emissions and energy impacts of building materials/technologies. We document detailed foreground data for selected building materials and building components that are common for building construction. To test the LCA methodology and the GREET Building Module, this report includes case studies of insulation materials and wall panels for residential building retrofit. We have developed a separate document as a User Guide for understanding and applying the GREET Building Module to conduct detailed, process-level LCA of embodied carbon and energy impacts of emerging building materials and technology solutions that of interest to the Building Technologies Office (BTO) of the US Department of Energy, researchers, and industry stakeholders.
This project aims to develop multi-scale building energy data, potentially improving the representation of the U.S. buildings sector in GCAM-USA, an U.S.-focused human-energy-Earth systems model. Existing building energy datasets are typically limited to national or regional levels, which constrains the ability of models to capture fine-scale human-energy-Earth systems interactions and reduces their relevance for decision-making on issues such as energy security, resilience, and energy planning. By leveraging AI and advanced data integration methods, this work fuses multiple existing datasets to enhance the physical and geographic representation of both residential and commercial building energy use. So far, progress includes processing residential building data, designing the data structure for commercial buildings, and testing AI approaches for integrating datasets and addressing spatial-temporal gaps. This effort can not only advances GCAM-USA’s capability in modeling the buildings sector but also supports broader DOE missions, such as developing digital testbeds, enhancing grid resilience analysis, and improving building–energy system modeling at decision-relevant scales.
Building Performance Standards (BPS) are gaining national traction. A BPS will require new processes in the design, construction, and operation of buildings that take the occupants into account and enable predictive analysis to ensure compliance with current and future GHG emissions caps. In New York City, most buildings over 25,000 square feet will be regulated by a BPS starting in 2024, regardless of whether it is new construction permitted under current energy codes or an existing building. This research is one of the first to begin the evaluation of a long-term series of building policies in the context of an open data ecosystem, in cooperation with city agencies. Existing building policies enacted in NYC have ranged from building energy benchmarking and labeling to energy audits to the regulation of GHG emission in buildings. Through the development of a dataset related to building technologies and energy consumption, this project can help to evaluate if meaningful conclusions can be drawn for the data that has been largely self-reported in compliance with city regulations. This project will also provide lessons learned from a deep dive into these types of datasets to provide best practices for municipalities or states seeking to embark on policies like those enacted in NYC. In addition, a Building Automation System (BAS) Stretch Standard of Care (SSOC) for owners, designers, and building operators will enable the measurement and predictive analysis of energy consumption and GHG emissions at the plant, system, or component level, in anticipation of regulated GHG limits on buildings based on energy use. The SSOC is expected to be suitable for use on a national level. The primary feature of an SSOC is a standardized format for a set of BAS points that can be used to control and to gather data from individual plants, systems, or components that are related to building energy consumption. This project examined how measurements compare to prescriptive or simulation-based energy code targets, finding little correlation between predictive 8760-hour energy modeling and actual energy consumption for a small sample (n=27) of buildings constructed after 2015. Other analysis found that, while large multifamily housing (MFH) buildings showed a general trend similar to predicted reductions in energy use from the implementation of model commercial energy codes, this trend was not evident in the office, K-12 school, and hotel use groups in NYC. No upward or downward trends in energy consumption were found when buildings were grouped by size. Energy audit data were analyzed and it appears that there is bias by audit company on measures recommended to clients. Further research should be performed to cross-analyze this with other attributes, such as building size, vintage, and number of stories. Analysis found that for 281 buildings that were permitted and completed after 2015 and had submitted benchmarking data in 2022, between 81% and 96% (by use group) were found to be in compliance with the 2024 to 2029 NYC BPS emission caps, and between 55% and 89% were in compliance with the 2030-2034 caps. This work is beneficial to the public in helping policymakers and building stakeholders better understand the wide-ranging implications of a BPS.
The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.