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The objectives of this project are as follows: 1) Reduce uncertainty in PV performance models by developing and validating new and improved models and submodes. 2) Create and manage an open source repository of modeling functions and data. 3) Build and grow the PV Performance Modeling Collaborative 4) Represent the US in the IEA PVPS Task 13 Working group.
An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.
Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.
Sandia National Laboratories (SNL) Employee Health Services (EHS) program believes that good health is essential to getting the most out of life, which is why we offer a variety of worksite wellness programs that put employees in control of their own health. These programs are based on current research and strategies that are proven to minimize risk and decrease the impact of illness through early detection and treatment. Services includes Health Assessments, the Virgin Pulse (VP) online wellness platform, and Health Action Plans (HAP) which include one-on-one education, organizational health initiatives, a video library, an events calendar, onsite fitness facilities, and group fitness classes. Participation in these programs help employees and their spouses earn funding for their Health Reimbursement Accounts (HRAs). The EHS Health Action Plan (HAP) initiative targets the key health risks identified through the 8-15-80 model by engaging employees to change their own healthcare story. EHS identified the most prevalent of the health risks amongst our population through Health Risk Assessment (HRA) and used that data to build Health Action Plans (HAPs). The 2020 Health Action Plans addressed improving upon inadequate sleep, lack of energy, stress, weight, physical inactivity, cardiometabolic issues (hypertension, high cholesterol, diabetes), low back pain, allergies, asthma, digestive health, tobacco use, and living well to maintaining low risk for those individuals who do not have a chronic condition to manage. These plans connect employees with our onsite registered dietitians, fitness professionals, health coaches, physical therapists, and physicians as appropriate. In addition to addressing the physical aspects of health, the plans also emphasized pre/post-assessments to encourage building behavioral and emotional skills that promote health and facilitate lifestyle changes over a minimum of three months. Just one risk reduction or behavior change can make an impact on the health of the participant’s Division as well as Sandia’s overall risk levels and ultimately its healthcare costs. Sandia employees achieved an Overall Wellness Score of 69 based on the WellSource Health Assessment (the same as last year). A score of 70-100 is considered “Doing Well”, and a score of 40-69 is in the “Caution” category. Overall in CY 2019, Sandia saw a 34% participation rate in Health Action Plan (HAP) programs (up from 33% in CY 2019) with an 89% completion rate (4% lower than CY 2019). Overall, 5,039 (347 more than CY 2019) individuals participated in 8,329 HAPs, which is 1,005 more than the last calendar year (7,324).
Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.
How can we use biometric data to build better human-machine interfaces?
In 2018 13.7 EJ of fuel were consumed by the global commercial aviation industry. Worldwide, demand will increase into the foreseeable future. Developing Sustainable Aviation Fuels (SAFs), with decreased CO 2 and soot emissions, will be pivotal to the on-going mitigation efforts against global warming. Minimizing aromatics in aviation fuel is desirable because of the high propensity of aromatics to produce soot during combustion. Because aromatics cause o-rings to swell, they are important for maintaining engine seals, and must be present in at least 8 vol% under ASTM-D7566. Recently, cycloalkanes have been shown to exhibit some o-ring swelling behavior, possibly making them an attractive substitute to decrease the aromatic content of aviation fuel. Cycloalkanes must meet specifications for a number of other physical properties to be compatible with jet fuel, and these properties can vary greatly with the cycloalkane chemical structure, making their selection difficult. Building a database of structure-property relationships (SPR) for cycloalkanes greatly facilitates their furthered inclusion into aviation fuels. The work presented in this paper develops SPRs by building a data set that includes physical properties important to the aviation industry. The physical properties considered are energy density, specific energy, melting point, density, flashpoint, the Hansen solubility parameter, and the yield sooting index (YSI). Further, our data set includes cycloalkanes drawn from the following structural groups: fused cycloalkanes, n-alkylcycloalkanes, branched cycloalkanes, multiple substituted cycloalkanes, and cycloalkanes with different ring sizes. In addition, a select number of cycloalkanes are blended into Jet-A fuel (POSF-10325) at 10 and 30 wt%. Comparison of neat and blended physical properties are presented. One major finding is that ring expanded systems, those with more than six carbons, have excellent potential for inclusion in SAFs. Our data also indicate that polysubstituted cycloalkanes have higher YSI values.
The stellar velocity distribution function in the solar vicinity is reexamined using data from the Sloan Digital Sky Survey Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey’s DR16 and Gaia DR2. By exploiting APOGEE’s ability to chemically discriminate with great reliability the thin-disk, thick-disk, and (accreted) halo populations, we can, for the first time, derive the three-dimensional velocity distribution functions (DFs) for these chemically separated populations. We employ this smaller but more data-rich APOGEE+Gaia sample to build a data-driven model of the local stellar population velocity DFs and use these as basis vectors for assessing the relative density proportions of these populations over the 5 < R < 12 kpc and −1.5 < z < 2.5 kpc range as derived from the larger, more complete (i.e., all-sky, magnitude-limited) Gaia database. We find that 81.9% ± 3.1% of the objects in the selected Gaia data set are thin-disk stars, 16.6% ± 3.2% are thick-disk stars, and 1.5% ± 0.1% belong to the Milky Way stellar halo. We also find the local thick-to-thin-disk density normalization to be ρ {sub T}(R {sub ⊙})/ρ {sub t}(R {sub ⊙}) = 2.1% ± 0.2%, a result consistent with, but determined in a completely different way from, typical star-count/density analyses. Using the same methodology, the local halo-to-disk-density normalization is found to be ρ {sub H}(R {sub ⊙})/(ρ {sub T}(R {sub ⊙}) + ρ {sub t}(R {sub ⊙})) = 1.2% ± 0.6%, a value that may be inflated due to the chemical overlap of halo and metal-weak thick-disk stars.
Solar photovoltaic (PV) and battery energy storage system (BESS) technologies are two immediately available options for meeting U.S. electricity demands, which are increasing due to expansion of loads from end uses including data centers, buildings, vehicles, and factories. Globally, PV and BESS supply chains are dominated by products manufactured in China and elsewhere by Chinese companies. However, U.S. PV and BESS manufacturing have recently grown, with some capacity in each step of the PV supply chain, albeit not enough to currently meet demand with domestic manufacturing alone. This study analyzes U.S. PV supply chains and costs in 2024, for the crystalline silicon and cadmium telluride module supply chains. The full report additionally addresses the PV balance of system, inverter and BESS supply chains. The study concludes with an analysis of technology installation trends, government support for domestic manufacturing, manufacturing jobs, and the domestic content of PV systems installed in the United States in 2024.
Abstract not provided.
The United States Agency for International Development is drawing on U.S. Department of Energy national laboratory expertise to advance clean, resilient, and equitable energy systems in the developing world through the USAID-NREL Partnership.
Furthering our understanding of the prevalence and severity of issues that customers face when charging their electric vehicles (EVs) is crucial in order to improve the charging experience across the United States. This project utilizes web-scraping, machine leaning (ML), and natural language processing (NLP) techniques to analyze and categorize user-generated reviews. Selenium was used to build a data collection tool that can scrape vast amounts of user review data from the PlugShare website. Sentiment analysis was employed on this dataset in order to filter out negative reviews for further analysis. NLP techniques such as tokenization and word embedding were then used to convert user-written comments into a numerical format that a ML model can interpret. Multiple ML approaches are currently being explored in order to identify and categorize the charging issues being talked about in each review. Ultimately, the results from the ML model will be visualized and explained in a report on customer pain points to be delivered to the ChargeX Consortium, therefore revealing specific areas for improvement in the customer charging experience.
Furthering our understanding of the prevalence and severity of issues that customers face when charging their electric vehicles (EVs) is crucial in order to improve the charging experience across the United States. This project utilizes web-scraping, machine leaning (ML), and natural language processing (NLP) techniques to analyze and categorize user-generated reviews. Selenium was used to build a data collection tool that can scrape vast amounts of user review data from the PlugShare website. Sentiment analysis was employed on this dataset in order to filter out negative reviews for further analysis. NLP techniques such as tokenization and word embedding were then used to convert user-written comments into a numerical format that a ML model can interpret. Multiple ML approaches are currently being explored in order to identify and categorize the charging issues being talked about in each review. Ultimately, the results from the ML model will be visualized and explained in a report on customer pain points to be delivered to the ChargeX Consortium, therefore revealing specific areas for improvement in the customer charging experience.
Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. The load profiles in the same group will have similar characteristics at the same time step, so grid operators can send the grid service signal to the customer group with a higher chance to respond at that time step. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation and the results have proved the effectiveness of this method.
Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.
Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.
Building energy consumption accounts for 36% of the overall energy end use worldwide and is growing rapidly as developing countries continue to urbanize. Understanding the energy use at urban scale will lay the foundation for identification of energy efficiency opportunities to be deployed at speed. China has almost half of global new constructions and plays an important role in building suitability. However, an open source national building energy consumption database is not available in China. To provide data support for building energy consumptions, this paper used a simulation method to develop an urban building energy consumption database for a pilot city in Wuhan, China. First, residential, small, and large office building archetype energy models were created in EnergyPlus to represent typical building energy consumption in Wuhan. The baseline reference model simulation results were further validated using survey data from the literature. Second, stochastic simulations were conducted to consider different design parameters and occupants’ energy usage intensity scenarios, such as thermal properties of the building envelope, lighting power density, equipment power density, HVAC (heating, ventilation and air conditioning) schedule, etc. A building energy consumption database was generated for typical building archetypes. Third, data-driven regression analysis was conducted to support quick building energy consumption prediction using key high- level building information inputs. Finally, a web-based urban energy platform and an interface were developed to support further third-party application development. The research is expected to provide fast energy efficiency building design solutions for urban planning, new constructions as well as building retrofits.