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DOE OSTI2023

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗

DOE OSTI2025

Integrated System for Methane Emissions Monitoring, Mapping, and Quantification

This report presents the work completed under the DOE iM4 project for the development of a methane emission monitoring system for detection, location, and quantification of methane in oil and gas industries. The task was divided into four main areas including: 1) Sensors and Input, 2) Centralized Cloud Information Center, 3) Algorithms, and 4) Testing and Validation. Task 1 focused on researching and developing an understanding of the current, or soon to be, available methane sensing technologies. Task 2 consisted of developing the architecture, selecting hardware, software and elements for the methane monitoring system. Task 3 focused on the algorithms used for the complex inverse model of going from measured methane signatures to the detection, localization, and quantification of sources that are desired. Finally, Task 4 focused on the methods of testing and validating the operation of the system. Attention was also given to the development method and cost breakdown of the system.

03 NATURAL GAS↗

DOE OSTI2020

Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies

Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock - both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

The Astronomy Commons Platform: A Deployable Cloud-based Analysis Platform for Astronomy

Abstract We present a scalable, cloud-based science platform solution designed to enable next-to-the-data analyses of terabyte-scale astronomical tabular data sets. The presented platform is built on Amazon Web Services (over Kubernetes and S3 abstraction layers), utilizes Apache Spark and the Astronomy eXtensions for Spark for parallel data analysis and manipulation, and provides the familiar JupyterHub web-accessible front end for user access. We outline the architecture of the analysis platform, provide implementation details and rationale for (and against) technology choices, verify scalability through strong and weak scaling tests, and demonstrate usability through an example science analysis of data from the Zwicky Transient Facility’s 1Bn+ light-curve catalog. Furthermore, we show how this system enables an end user to iteratively build analyses (in Python) that transparently scale processing with no need for end-user interaction. The system is designed to be deployable by astronomers with moderate cloud engineering knowledge, or (ideally) IT groups. Over the past 3 yr, it has been utilized to build science platforms for the DiRAC Institute, the ZTF partnership, the LSST Solar System Science Collaboration, and the LSST Interdisciplinary Network for Collaboration and Computing, as well as for numerous short-term events (with over 100 simultaneous users). In a live demo instance, the deployment scripts, source code, and cost calculators are accessible. 4 4 http://hub.astronomycommons.org/

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Improving the Capabilities and Computational Efficiency of the RTE+RRTMGP Radiation Code (Final Report)

This report details progress on the RTE+RRTMGP radiation codes made during the period of performance. RTE+RRTMGP is a set of codes for computing radiative fluxes in planetary atmospheres. RRTMGP uses a k-distribution to provide an optical description (absorption and possibly Rayleigh optical depth) of the gaseous atmosphere, along with the relevant source functions, on a pre-determined spectral grid given temperatures, pressures, and gas concentration. RTE computes fluxes given spectrally-resolved optical descriptions and source functions. Spectrally-resolved fluxes are summarized (“reduced”) via a user extensible class. The initial release of the code and the design choices are described in Pincus et al. 2019; the codes are available on Github. Although RRTMGP was based on current (at the time) empirical spectroscopic data, RTE and RRTMGP were developed in large part to modernize software practices. The design focused on flexibility broadly interpreted: by separating code from data and allowing data to drive computation; in coupling to the host model (e.g. the coupling of clouds to radiative fluxes is user-controlled); with respect to programming languages (computational tasks are accessed via widely-compatible C interfaces); and with respect to hardware (the codes run on a range of CPU and GPU architectures). The code also puts an emphasis on modularity and clarity. RTE+RRTMGP v1.0 was released in September 20219. This award supported the evolution of the RTE+RRTMGP code base to support greater flexibility, accuracy, and efficiency.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures

Federated learning is driving the development of new techniques to efficiently and securely use data across multiple sites while using diverse resources. One of these techniques is the use of the serverless computing paradigm to abstract away resource specific configurations, allowing federated learning across heterogeneous environments. However, deploying federated learning across edge resources, the cloud, and traditional HPC sites will require specialized approaches in order to best account for the weaknesses and strengths of each resource. In this work, we explore the new tradeoffs presented by managing a federated learning task across heterogeneous resources and demonstrate these tradeoffs with experiments using a serverless federated learning framework.

Baughman, Matt↗

DOE OSTI2024

Science Use Case Design Patterns for Autonomous Experiments

Connecting scientific instruments and robot-controlled laboratories with computing and data resources at the edge, the Cloud or the high-performance computing (HPC) center enables autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with a federated hardware/software architecture for the laboratory of the future. It relies on a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture. This paper introduces the science use case design patterns of the INTERSECT Architecture. It describes the overall background, the involved terminology and concepts, and the pattern format and classification. It further offers an overview of the 12 defined patterns and 4 examples of patterns of 2 different pattern classes. It also provides insight into building solutions from these patterns. The target audience are computer, computational, instrument and domain science experts working in the field of autonomous experiments.

Engelmann, Christian↗

DOE OSTI2021

Concentric Spherical GNN for 3D Representation Learning

Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resolution convolutional architecture for learning over concentric spherical feature maps, of which the single sphere representation is a special case. Our hierarchical architecture is based on alternatively learning to incorporate both intra-sphere and inter-sphere information. We show the applicability of our method for two different types of 3D inputs, mesh objects, which can be regularly sampled, and point clouds, which are irregularly distributed. We also propose an efficient mapping of point clouds to concentric spherical images, thereby bridging spherical convolutions on grids with general point clouds. We demonstrate the effectiveness of our approach in improving state-of-the-art performance on 3D classification tasks with rotated data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Applying corrective machine learning in the E3SM atmosphere model in C++ (EAMxx)

The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) is the newest addition to the family of earth system models capable of explicitly resolving convective systems. SCREAM is a kilometer-scale configuration of the advanced E3SM Atmosphere Model (EAMxx), designed for heterogeneous computing architectures. While the enhanced accuracy of kilometer-scale modeling offers significant benefits, it comes with a substantial computational cost, limiting feasible simulation durations to only a few years to a few decades, even on the fastest supercomputers. Machine learning presents an opportunity for scientists to achieve the high accuracy of storm-resolving models at a significantly reduced cost. Building on the previous success of applying corrective machine learning (ML) to the FV3GFS earth system model, this study explores the effects of implementing corrective-ML in EAMxx-SCREAM. We also address the computational challenges of integrating our implementation of corrective-ML, which is written in Python, with the C++/Kokkos EAMxx driver, as well as potential reasons why this approach has not proved as effective for EAMxx-SCREAM as for FV3GFS.

Environmental sciences↗

DOE OSTI2026

Oak Ridge National Laboratory Building Envelope Library (ORNOBEL)

The Oak Ridge National Laboratory Building Envelope Library (ORNOBEL) is a collection of dense exterior building-facade point clouds acquired using a survey-grade terrestrial laser scanner. Each file represents an individual facade from a building on the Oak Ridge National Laboratory (ORNL) campus or in Knoxville, Tennessee, with an average point-cloud resolution of approximately 3 mm. The points in each facade are semantically labeled into three classes: (1) window/door, representing openings in the building envelope; (2) wall, representing planar opaque envelope surfaces; and (3) other, representing the remaining facade-adjacent elements, architectural features, and protrusions. ORNOBEL supports the development, training, and evaluation of advanced deep-learning methods for automated building-envelope segmentation, geometric reconstruction, and building information modeling (BIM).

Maldonado Puente, Bryan [ORNL] (ORCID:000000033880↗

DOE OSTI2020

CEEP (Cyber-Energy Emulation Platform) [SWR-20-102]

NREL's Cyber-Energy Emulation Platform (CEEP) provides the capability to realize cyber-energy security and resilience through automation and orchestration of virtualized systems and software defined networks for the electric grid. CEEP enables testing and validation of grid-security and -control methodologies as the grid evolves to include smart technologies/systems, such as virtualization and containerization of grid components, software defined networking, simulation and co-simulation frameworks, and hardware in the loop. CEEP is a modular system that can be distributed and deployed across different hardware infrastructure sizes and network architectures. For example, CEEP can visualize, emulate, and/or coordinate the Smart-Grid Network Visualization, Intrusion Detection, and Network Healing system. Using CEEP, intrusion-detection and network-self-healing solutions can be deployed at grid control centers, within secure private clouds, and in cyber-energy appliances.

Vaughan, Evan↗

DOE OSTI2022

Cyber Energy Emulation Platform (CEEP) [SWR-20-102]

Rivera, Joshua↗

DOE OSTI2022

Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?

High-throughput structure-based screening of drug-like molecules has become a common tool in biomedical research. Recently, acceleration with graphics processing units (GPUs) has provided a large performance boost for molecular docking programs. Both cloud and high-performance computing (HPC) resources have been used for large screens with molecular docking programs; while NVIDIA GPUs have dominated cloud and HPC resources, new vendors such as AMD and Intel are now entering the field, creating the problem of software portability across different GPUs. Ideally, software productivity could be maximized with portable programming models that are able to maintain high performance across architectures. While in many cases compiler directives have been used as an easy way to offload parallel regions of a CPU-based program to a GPU accelerator, they may also be an attractive programming model for providing portability across different GPU vendors, in which case the porting process may proceed in the reverse direction: from low-level, architecture-specific code to higher-level directive-based abstractions. MiniMDock is a new mini-application (miniapp) designed to capture the essential computational kernels found in molecular docking calculations, such as are used in phar-maceutical drug discovery efforts, in order to test different solutions for porting across GPU architectures. Here we extend MiniMDock to GPU offloading with OpenMP directives, and compare to performance of kernels using CUDA and HIP on NVIDIA and AMD GPUs, respectively, as well as across different compilers, exploring performance bottlenecks. We document this reverse-porting process, from highly optimized device code to a higher-level version using directives, compare code structure, and describe barriers that were overcome in this effort.

Thavappiragasam, Mathialakan↗

DOE OSTI2025

An Evaluation of Representation Learning Methods in Particle Physics Foundation Models

We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-supervised), masked particle modeling, and generative reconstruction objectives under a common training regimen. In addition, we introduce targeted supervised architectural modifications that achieve state-of-the-art performance on benchmark evaluations. This controlled comparison isolates the contributions of the learning objective, highlights their respective strengths and limitations, and provides reproducible baselines. We position this work as a reference point for the future development of foundation models in particle physics, enabling more transparent and robust progress across the community.

Chen, Michael [Caltech]↗

DOE OSTI2024

MatRIS: Addressing the Challenges for Portability and Heterogeneity Using Tasking for Matrix Decomposition (Cholesky)

The ubiquitous in-node heterogeneity of HPC and cloud computing platforms makes software portability and performance optimization extremely challenging. Described here, the MatRIS multilevel math library abstraction framework employs tasking to alleviate these difficulties. MatRIS includes the IRIS task-based runtime on the bottom level and exposes different layers of abstraction to render algorithms architecturally agnostic. MatRIS ensures the decomposition and creation of tasks that represent the necessary encapsulation of the optimized kernels from both vendor and open-source math libraries. Once built, MatRIS can select different combinations of accelerators at runtime, making it portable even on diverse heterogeneous architectures. By leveraging the IRIS runtime’s features for managing heterogeneity, MatRIS deploys algorithms that remove the need to specify orchestration and data transfer. This study describes how the serial task abstraction of a tiled Cholesky factorization is made portable and scalable in the case of multi-device and multi-vendor heterogeneity on a node with NVIDIA and AMD GPUs by using MatRIS. First, we demonstrate that Cholesky in MatRIS provides multi-GPU scalability that offers competitive performance versus cuSolverMG. Then, we present the challenges and opportunities for heterogeneous execution.

Monil, M. A. H.↗

DOE OSTI2022

Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications

This report summarizes the Year 1 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research Program. 5G is a breakthrough technology that enables a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for a clean energy economy and grid modernization. In collaboration with PNNL’s Advanced Wireless Communication team and Center for Advanced Technology Evaluation team, the project team has been evaluating the system performance of 5G testbeds in the PNNL 5G Innovation Studio, and has formulated a co-simulation test case of power system transmission, distribution, and communication (T&D&C) networks considering 5G technology and high penetration of distributed energy resources. The methodology developed in the 5G Energy FRAME project can be customized to fit different future grid scenarios to evaluate multiple (dynamic) configurations (computing, sensing, communication, environment) for different stakeholders. In summary, our main technical highlights in project Year 1 are as follows: 1) Technical characterization of 5G standalone architectures, 2) Formulation of co-simulation test case of T&D&C networks embedded with 5G, 3) Initial benefit evaluation of 5G communication platform for grid use cases, and 4) Additional extended discussions on edge computing, artificial intelligence and machine learning, and high-performance computing and cloud computing adoptions. In addition, a collection of system performance data is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput

The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. Here, by reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.

AWS↗

DOE OSTI2024

RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring

Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.

60 APPLIED LIFE SCIENCES↗