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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate

Understanding the structure and thermodynamics of solvated ions is essential for advancing applications in electrochemistry, water treatment, and energy storage. While ab initio molecular dynamics methods are highly accurate, they are limited by short accessible time and length scales whereas classical force fields struggle with accuracy. Herein, we explore the structure and thermodynamics of complex monovalent-divalent ion pairs using Na 2 SO 4 (aq) as a case study by applying a machine learning interatomic potential (MLIP) trained on density functional theory (DFT) data. Our MLIP-based approach reproduces key bulk properties such as density and radial distribution functions of water. We provide the hydration structure of the sodium and sulfate ions in the 0.1–2 M concentration range and the one-dimensional and two-dimensional potentials of mean force for the sodium–sulfate ion pairing at the low concentration limit (0.1 M), which are inaccessible to DFT. At low concentrations, the sulfate ion is strongly solvated, leading to the stabilization of solvent-separated ion pairs over contact ion pairs. Minimum energy pathway analysis revealed that coordinating two sodium ions with a sulfate ion is a multistep process whereby the sodium ions coordinate to the sulfate ion sequentially. Finally, we demonstrate that MLIPs allow the study of solvated ions beyond simple monovalent pairs with DFT-level accuracy in their low concentration limit (0.1 M) via statistically converged properties from ns-long simulations.

anions↗
DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Software-Hardware Co-design of Heterogeneous SmartNIC System for Recommendation Models Inference and Training

Deep Learning Recommendation Models (DLRMs) are critical applications in various domains and have evolved as one of the single largest machine learning applications. Trillions of DLRM parameters exceed the on-chip memory capacity of GPUs. Large-scale multi-node systems are required for distributed DLRM inference and training, which suffer from the all-to-all communication bottleneck, mainly limiting the scalability of ever-growing DLRMs. In recent years, SmartNICs have evolved with coupled computation and communication capabilities providing opportunities for a powerful heterogeneous device in the system. However, there isn't such a distributed system that fully leverages the abundant smartNIC resources that resolve the scalability issue of DLRMs. In this work, we proposed a software-hardware co-design of a heterogeneous smartNIC system that resolves the communication bottleneck of distributed DLRMs, mitigates the memory bandwidth pressure, and improves computation efficiency. We provide a set of smartNIC designs of cache systems (including local cache and remote cache) and smartNIC computation kernels which reduce data movement, relieve memory lookup intensity, and improve the GPU's computation efficiency. In addition, we propose a graph algorithm that improves the data locality of queries within batches which optimizes the overall system performance with higher data reuse. Our evaluation shows that our system achieves 2.1x latency speedup for inference and 1.6x throughput speedup for training.

Guo, Anqi↗
DOE OSTI2025

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗
DOE OSTI2025

Machine Learning Enabled Process Optimization for Pharmacologically Relevant Dependent Variables: CRADA Final Report

ABPDU staff worked with Teselagen to provide expertise and perform literature searches for therapeutic carrying strains to maximize fermentation parameters for Teselagen's machine learning platform. The goal was to overcome the COVID-19 crisis and plan for a future where molecules can be detected, diagnosed, built, manufactured, and distributed within months.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

BrazilClim : The overcoming of limitations of pre‐existing bioclimate data

Abstract Species distribution modelling has become instrumental in assessing the influence of environmental conditions on the occurrence or abundance of taxa. The set of environmental layers used for this purpose is a crucial aspect, for which different climate‐based (bioclimatic) datasets have been recently developed. These bioclimatic variables result from combinations of precipitation and temperatures surfaces. Here, we explored both the performance and possibility of improving some of the currently available bioclimatic databases, through an evaluation of the precipitation and temperatures surfaces used to generate them. For this purpose, we used a combination of statistic and graphic approaches. We focused on Brazil, not only due to its natural megadiversity, but also due to its continental size and orographic heterogeneity: an excellent ground for refining methods replicable elsewhere. We found a better match between the climatic data measured on‐field and Tropical Rainfall Measuring Mission (TRMM 3B43 v7) in the case of precipitation, and the surfaces provided by the National Oceanic and Atmospheric Administration (NOAA) in the case of temperatures, sources uncommonly used for species niche modelling. We gauge‐calibrated the best performing surfaces using machine‐learning algorithms and generated corrected surfaces that allowed us to create BrazilClim: a database of bioclimatic variables, based on improved primary surfaces, which will result in more assertive predicted distributions and more actual pictures of the species' ecological requirements for megadiverse Brazil, an approach replicable elsewhere. All primary and bioclimatic surfaces generated for this study may be freely downloaded.

Ramoni‐Perazzi, Paolo↗
DOE OSTI2024

An Evaluation of Sustainable Power System Resilience in the Face of Severe Weather Conditions and Climate Changes: A Comprehensive Review of Current Advances

Natural disasters pose significant threats to power distribution systems, intensified by the increasing impacts of climate changes. Resilience-enhancement strategies are crucial in mitigating the resulting social and economic damages. Hence, this review paper presents a comprehensive exploration of weather management strategies, augmented by recent advancements in machine learning algorithms, to show a sustainable resilience assessment. By addressing the unique challenges posed by diverse weather conditions, we propose flexible and intelligent solutions to navigate disaster complications effectively. This proposition emphasizes sustainable practices that not only address immediate disaster complications, but also prioritize long-term resilience and adaptability. Furthermore, the focus extends to mitigation strategies and microgrid technologies adapted to distribution systems. Through statistical analysis and mathematical formulations, we highlight the critical role of these advancements in mitigating severe weather conditions and ensuring the system reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

WE-AB-BRB-00: Session in Memory of Robert J. Shalek: High Resolution Dosimetry from 2D to 3D to Real-Time 3D

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Data associated with “Different methods of estimating riverbed sediment grain size diverge at the basin scale ” (v2)

This data package is associated with the publication “Different methods of estimating riverbed sediment grain size diverge at the basin scale” published in Frontiers in Earth Science (Regier et al., 2025). The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (d50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual methods to measure d50 are time-consuming and ignore larger grains, while model-based methods to estimate d50 often over-generalize basin characteristics, and therefore cannot accurately represent site-scale heterogeneity. Here, we apply a machine learning-enabled photogrammetry methodology (You Only Look Once, or YOLO) for estimating d50 for grains > 2 mm based on images collected from streams and rivers throughout the Yakima River Basin (YRB). To understand how such methods may help bridge the gaps in resolution and accuracy between manual and catchment characteristics model-based d50 estimates, we compared YOLO d50 values to manual and model-based estimates across the YRB. We found distinct differences among methods for d50 averages and variability, and relationships between d50 estimates and basin characteristics. Source images can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052. This data package was originally published in May 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. In addition to the readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) and subfolders containing data, figures, and scripts. The data folder contains datasets used for the analyses in the manuscript in image, text-delimited or geospatially-referenced formats. The figures folder contains the figures from the manuscript in different formats. The scripts folder contains all of the scripts used to complete the analyses in the manuscript. All files are .csv, .rds, .dbf, .prj, .shp, .shx, .jpg, .png, .R, .Rproj, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

WE-AB-BRB-01: Memorial Introduction; Storage Phosphor Panels for Radiation Therapy Dosimetry

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-BRB-02: Methods and Applications of 3D Radiochromic Dosimetry

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-BRB-03: Real-Time Volumetric Scintillation Dosimetry for Radiation Therapy

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Signal-preserving CMB component separation with machine learning

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained. Published by the American Physical Society 2025

McCarthy, Fiona (ORCID:0000000253893565)↗
DOE OSTI2016

WE-AB-BRB-04: Cherenkov Imaging for Radiation Therapy Dose Verification On Patients

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The EGS Collab project: Outcomes and lessons learned from hydraulic fracture stimulations in crystalline rock at 1.25 and 1.5 km depth

With the goal of better understanding stimulation in crystalline rock for improving enhanced geothermal systems (EGS), the EGS Collab Project performed a series of stimulations and flow tests at 1.25 and 1.5 km depths. The tests were performed in two well-instrumented testbeds in the Sanford Underground Research Facility in Lead, South Dakota, United States. The testbed for Experiment 1 at 1.5 km depth contained two open wells for injection and production and six instrumented monitoring wells surrounding the targeted stimulation zone. Four multi-step stimulation tests targeting hydraulic fracturing and nearly year-long ambient temperature and chilled water flow tests were performed in Experiment 1. The testbed for Experiments 2 and 3 was at 1.25 km depth and contained five open wells in an outwardly fanning five-spot pattern and two fans of well-instrumented monitoring wells surrounding the targeted stimulation zone. Experiment 2 targeted shear stimulation, and Experiment 3 targeted low-flow, high-flow, and oscillating pressure stimulation strategies. Hydraulic fracturing was successful in Experiments 1 and 3 in generating a connected system wherein injected water could be collected. However, the resulting flow was distributed dynamically, and not entirely collected at the anticipated production well. Thermal breakthrough was not observed in the production well, but that could have been masked by the Joule-Thomson effect. Shear stimulation in Experiment 2 did not occur - despite attempting to pressurize the fractures most likely to shear - because of the inability to inject water into a mostly-healed fracture, and the low shear-to-normal stress ratio. The EGS Collab experiments are described to provide a background for lessons learned on topics including induced seismicity, the correlation between seismicity and permeability, distributed and dynamic flow systems, thermoelastic and pressure effects, shear stimulation, local geology, thermal breakthrough, monitoring stimulation, grouting boreholes, modeling, and system management.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2019

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

25 ENERGY STORAGE↗