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DOE OSTI2019

Site Characterization and Adaptation - Lessons Learned from the Port Granby LLRW Project in Canada - 19255

The Port Hope Area Initiative (PHAI) is a community-based solution for the long-term management of historic low level radioactive waste (LLRW) resulting from 60 years of uranium and radium processing operations in the Town of Port Hope, Ontario. The Eldorado refinery, on the shores of Lake Ontario, began refining radium-226 from pitchblende ore, later transitioning to the refining of uranium ore. Process residues were deposited at the Welcome Waste Management Facility in the Town of Port Hope until the mid-1950's switching to the Port Granby Waste Management Facility (PGWMF), which continued to accept waste until the late 1980's. The Port Granby Project is part of the broader PHAI being completed by Canadian Nuclear Laboratories (CNL). The scope of remediation activities at Port Granby includes: 1) constructing a new, secure long-term waste management facility (LTWMF) and supporting infrastructure, and 2) excavating and relocating LLRW and marginally contaminated soil from the Port Granby Waste Management Facility (PGWMF) to the newly constructed LTWMF. Remediation design required extensive site investigations of waste buried in unmarked and unlined trenches across the PGWMF area. Investigations included reviews of historical records, interviews with past staff of Eldorado and Atomic Energy Canada Limited (the entity managing the PHAI cleanup prior to CNL), radiological surveys, and the advancement of boreholes across the site to facilitate the physical sampling of soil and groundwater contaminants indicative of the presence of LLRW. This paper describes the challenges associated with characterizing and quantifying waste prior to remediation and lessons-learned to adjust procedural adaptations during the characterization phase of a project to better anticipate the unanticipated nature and growth of the contamination profile that often occurs during remediation. Despite substantial pre-remediation investigative efforts, current PGWMF remediation activities have resulted in growth of both the areal and vertical extent of contamination. The contamination profile was shown to be dramatically influenced by leaching of contaminants deeper into native soils. In addition, remedial excavation discovered specific waste types such as unidentified compressed gas cylinders outside of the location indicated by historical records, as well as a quantity of unknown liquids and unmarked drums. These discoveries have resulted in higher waste quantity estimates and have required a partial redesign of the LTWMF mound (a higher final elevation and steeper slopes) to accommodate the increased waste volume. In addition, the means and methods necessary to execute the remedial construction had to be modified to account for the new conceptual model developed from the new contamination data. Despite extensive historical review and site characterization to define the nature and extent of LLRW buried at the PGWMF, actual conditions encountered during remediation efforts necessitated taking an adaptive approach to both the design of the LTWMF and the means and methods used for excavation and transport of waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

An Open-Source Framework for Characterizing Urban Energy Models: Integrating Top-Down and Bottom-Up Methods to Predict Residential Buildings Characteristics: Preprint

Bottom-up urban energy models are crucial for understanding current energy use patterns and informing design strategies. However, accurately characterizing these models to represent different communities remains a challenge due to the extensive data needed for simulating existing energy use behavior. This data includes information related to human activities and building characteristics, all of which correlate with socioeconomic factors. To overcome this challenge, we developed an automated framework that utilizes both top-down and bottom-up data, to predict unknown building and occupant characteristics that are needed for more accurate and equitable modeling and analytics. Our framework, integrated into the URBANopt district energy modeling platform, uses statistical data models from ResStock. URBANopt models co-located buildings and neighborhoods. At this scale there are data gaps in building characteristic data, such as materials, insulation, occupancy, income, and energy usage of the buildings. To address this data gap, we use ResStock data, representative at the census tract scale, and develop machine-learning and deeplearning techniques to disaggregate it to individual buildings. By mapping unique occupant, building and economic properties to URBANopt energy models, we gain detailed insights into the variability of building energy use across different neighborhoods. This insight helps deploy technologies for co-located buildings and supports targeted upgrades for communities with unique economic and demographic characteristics, ensuring energy equity. Accurate characterization of energy models allows us to develop equitable strategies tailored to diverse neighborhoods, whether underserved or affluent. Our automated framework streamlines energy modeling and provides a reliable tool for building energy characterization.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2025

From Molecules to Modules: Advanced Characterization of Membrane Systems

Membrane technologies can enhance the efficiency and selectivity of chemical separations in energy-water systems. Advanced characterization tools are critical for discerning separation mechanisms, revealing degradation processes, and designing novel materials and material systems for new and emerging challenges. The pursuit of next-generation membranes for water and energy applications requires understanding phenomena at the molecular scale, mesoscale, and macroscale. This perspective highlights advanced characterization techniques for elucidating and enhancing membrane performance, while addressing fundamental trade-offs involved in characterizing membranes under realistic conditions.

Zhu, Yaguang [Princeton University, NJ (United Sta↗

DOE OSTI2019

Snapshots of a Migrating H‐Atom: Characterization of a Reactive Iron(III) Indenide Hydride and its Nearly Isoenergetic Ring‐Protonated Iron(I) Isomer

Abstract We report the characterization of an S = iron π‐complex, [Fe(η 6 ‐IndH)(depe)] + (Ind=Indenide (C 9 H 7 − ), depe=1,2‐bis(diethylphosphino)ethane), which results via C−H elimination from a transient Fe III hydride, [Fe(η 3 :η 2 ‐Ind)(depe)H] + . Owing to weak M−H/C−H bonds, these species appear to undergo proton‐coupled electron transfer (PCET) to release H 2 through bimolecular recombination. Mechanistic information, gained from stoichiometric as well as computational studies, reveal the open‐shell π‐arene complex to have a BDFE C‐H value of ≈50 kcal mol −1 , roughly equal to the BDFE Fe‐H of its Fe III −H precursor (Δ G °≈0 between them). Markedly, this reactivity differs from related Fe(η 5 ‐Cp/Cp*) compounds, for which terminal Fe III −H cations are isolable and have been structurally characterized, highlighting the effect of a benzannulated ring (indene). Overall, this study provides a structural, thermochemical, and mechanistic foundation for the characterization of indenide/indene PCET precursors and outlines a valuable approach for the differentiation of a ring‐ versus a metal‐bound H‐atom by way of continuous‐wave (CW) and pulse EPR (HYSCORE) spectroscopic measurements.

Drover, Marcus W.↗

DOE OSTI2019

Snapshots of a Migrating H‐Atom: Characterization of a Reactive Iron(III) Indenide Hydride and its Nearly Isoenergetic Ring‐Protonated Iron(I) Isomer

Drover, Marcus W.↗

DOE OSTI2021

Cloning and characterization of a panel of mitochondrial targeting sequences for compartmentalization engineering in Saccharomyces cerevisiae

Mitochondrion is generally considered as the most promising subcellular organelle for compartmentalization engineering. Much progress has been made in reconstituting whole metabolic pathways in the mitochondria of yeast to harness the precursor pools (i.e., pyruvate and acetyl-CoA), bypass competing pathways, and minimize transportation limitations. However, only a few mitochondrial targeting sequences (MTSs) have been characterized (i.e., MTS of COX4), limiting the application of compartmentalization engineering for multigene biosynthetic pathways in the mitochondria of yeast. In the present study, based on the mitochondrial proteome, a total of 20 MTSs were cloned and the efficiency of these MTSs in targeting heterologous proteins, including the Escherichia coli FabI and enhanced green fluorescence protein (EGFP) into the mitochondria was evaluated by growth complementation and confocal microscopy. After systematic characterization, six of the well!performed MTSs were chosen for the colocalization of complete biosynthetic pathways into the mitochondria. As proof of concept, the full alpha-santalene biosynthetic pathway consisting of 10 expression cassettes capable of converting acety coA to alpha-santalene was compartmentalized into the mitochondria, leading to a 3.7 fold improvement in the production of alpha-santalene. Furthermore, the newly characterized MTSs should contribute to the expanded metabolic engineering and synthetic biology toolbox for yeast mitochondrial compartmentalization engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Synthesis and Characterization of a Masked Terminal Nickel-Oxide Complex

In exploring terminal nickel-oxo complexes, postulated to be the active oxidant in natural and non-natural oxidation reactions, we report the synthesis of the pseudo-trigonal bipyramidal Ni II complexes (K)[Ni II (L Ph )(DMF)] (1[DMF]) and (NMe 4 ) 2 [Ni II (L Ph )(OAc)] (1[OAc]) (L Ph =2,2’,2’’-nitrilo-tris-(N-phenylacetamide); DMF=N,N-dimethylformamide; - OAc=acetate). Both complexes were characterized using NMR, FTIR, ESI-MS, and X-ray crystallography, showing the L Ph ligand to bind in a tetradentate fashion, together with an ancillary donor. The reaction of 1[OAc] with peroxyphenyl acetic acid (PPAA) resulted in the formation of [(L Ph )Ni III -O-H∙∙∙OAc] 2- , 2, that displays many of the characteristics of a terminal Ni=O species. 2 was characterized by UV-Vis, EPR, and XAS spectroscopies and ESI-MS. 2 decayed to yield a Ni II -phenolate complex 3 (through aromatic electrophilic substitution) that was characterized by NMR, FTIR, ESI-MS, and X-ray crystallography. 2 was capable of hydroxylation of hydrocarbons and epoxidation of olefins, as well as oxygen atom transfer oxidation of phosphines at exceptional rates. While the oxo-wall remains standing, this complex represents an excellent example of a masked metal-oxide that displays all of the properties expected of the ever elusive terminal M=O beyond the oxo-wall.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Single Atom Catalysts: A Review of Characterization Methods

Abstract Single atom catalysts (SACs) harbor a potential to exceed nanoparticle catalysts in terms of activity, stability and selectivity in a growing number of chemical reactions. Although their investigation is attracting significant attention, important fundamental questions focusing on key physicochemical properties of SACs (e. g., structure – property relationships, structural dynamics, reaction‐driven restructuring) remain unanswered. A main challenge for research in the field is how to reliably characterize the environments of single atoms in the presence of complicating factors such as low weight loadings, strong metal‐support interactions, and atomic and multiscale heterogeneity of bonding in the single atom sites. This review addresses this challenge – identifying catalytically relevant features of physicochemical properties of single atoms (charge state, electronic structure, atomic configuration, bonding interactions with a support) and surveying advanced tools/methods for characterizing them. The review places a strong emphasis on multimodal methods exploiting X‐ray absorption, emission and photoelectron spectroscopies, and provides several examples from the authors’ research that demonstrate their use as powerful tools for SAC characterization.

Kottwitz, Matthew↗

DOE OSTI2023

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Finally, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗

DOE OSTI2019

Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges

Characterization of a parallel application’s communication patterns can be useful for performance analysis, debugging, and system design. However, obtaining and interpreting a characterization can be difficult. AChax implements an approach that uses search and a library of known communication patterns to automatically characterize communication patterns. Our approach has some limitations that reduce its effectiveness for the patterns and pattern combinations used by some real-world applications. By viewing AChax’s pattern recognition problem as an image recognition problem, it may be possible to use deep learning to address these limitations. In this position paper, we present our current ideas regarding the benefits and challenges of integrating deep learning into AChax and our conclusion that a hybrid approach combining deep learning classification, regression, and the existing AChax approach may be the best long-term solution to the problem of parameterizing recognized communication patterns.

Roth, Philip↗

DOE OSTI2019

Characterization of renal stone composition by using fast kilovoltage switching dual-energy computed tomography compared to laboratory stone analysis: a pilot study

Purpose: To prospectively examine the diagnostic performance of fast kilovoltage switching dual-energy computed tomography (DECT) in characterization of in vivo renal stone composition compared with postoperative stone analysis. Methods: Consecutive consenting patients scheduled for endoscopic kidney stone surgery in a tertiary referral hospital from June 2015 to January 2016 were enrolled. Patients were preoperatively scanned with single-source, fast kilovoltage switching DECT. Stone compositions were determined regarding the effective atomic number measurements. Results of the stone compositions from DECT were compared to postoperative infrared spectroscopy stone analysis as the standard reference. Results: For the 39 patients enrolled in the study, DECT was able to detect uric acid stone with sensitivity of 88.9% and specificity of 100%. There was 100% positive predictive value, 96.8% negative predictive value, and 97.4% accuracy. For non-contrast CT scan, sensitivity was 88.9%, specificity was 96.7%, positive predictive value was 88.9%, negative predictive value was 96.7%, and accuracy was 94.8%. Of the 39 samples examined, 21 (54%) were single composition, whereas 18 (46%) were combined. Single composition stones were correctly characterized by DECT in 100% (8/8) for uric acid. Whereas the result of uric acid stone containing stone discrimination in mixed composition was not so good with Z{sub eff} alone, iodine imaging can compensated this fault. Conclusions: DECT provides excellent accuracy in characterizing uric acid stone compositions. With the addition of iodine image, all of uric acid-containing stones can be determined by the DECT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Atmo-ecometabolomics: A novel atmospheric particle chemical characterization methodology for ecological research

Aerosol particles play important roles in processes controlling the composition of the atmosphere and function of ecosystems. A better understanding of the composition of aerosol particles is beginning to be recognized as critical for ecological research to further comprehend the link between aerosols and ecosystems. While chemical characterization of aerosols has been practiced in the atmospheric science community, detailed methodology tailored to the needs of ecological research does not exist yet. In this study, we describe an efficient methodology (atmo-ecometabolomics), in step-by-step details, from the sampling to the data analyses, to characterize the chemical composition of aerosol particles, namely atmo-metabolome. This method employs mass spectrometry platforms such as liquid and gas chromatography mass spectrometries (MS) and Fourier transform ion cyclotron resonance MS (FT-ICR-MS). For methodology evaluation, we analyzed aerosol particles collected during two different seasons (spring and summer) in a low-biologicalactivity ecosystem. Additionally, to further validate our methodology, we analyzed aerosol particles collected in a more biologically active ecosystem during the pollination peaks of three different representative tree species. Our statistical results showed that our sampling and extraction methods are suitable for characterizing the atmo-ecometabolomes in these two distinct ecosystems with any of the analytical platforms. Datasets obtained from each mass spectrometry instrument showed overall significant differences of the atmo-ecometabolomes between spring and summer as well as between the three pollination peak periods. Furthermore, we have identified several metabolites that can be attributed to pollen and other plant-related aerosol particles.We additionally provide a basic guide of the potential use ecometabolomic techniques on different mass spectrometry platforms to accurately analyze the atmoecometabolomes for ecological studies. Our method represents an advanced novel approach for future studies in the impact of aerosol particle chemical compositions on ecosystem structure and function and biogeochemistry.

Rivas-Ubach, Albert (ORCID:0000000312937127)↗

DOE OSTI2024

Characterization of Two Fast-Turnaround Dry Dilution Refrigerators for Scanning Probe Microscopy

Low-temperature scanning probe microscopes (SPMs) are critical for the study of quantum materials and quantum information science. Due to the rising costs of helium, cryogen-free cryostats have become increasingly desirable. However, they typically suffer from comparatively worse vibrations than cryogen-based systems, necessitating the understanding and mitigation of vibrations for SPM applications. Here, in this work, we demonstrate the construction of two cryogen-free dilution refrigerator SPMs with minimal modifications to the factory default and we systematically characterize their vibrational performance. We measure the absolute vibrations at the microscope stage with geophones and use both microwave impedance microscopy and a scanning single-electron transistor to independently measure tip-sample vibrations. Additionally, we implement customized filtering and thermal anchoring schemes and characterize the cooling power at the scanning stage and the tip electron temperature. This work serves as a reference to researchers interested in cryogen-free SPMs, as such characterization is not standardized in the literature or available from manufacturers.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Characterization of Novel and Efficient Poly-3-hydroxybutyrate (PHB) Producing Bacteria Isolated from Rhizospheric Soils

Polyhydroxybutyrate (PHB) is an eco-friendly, biodegradable plastic which exhibits properties very similar to the conventional plastic, thus, having high potential for replacing non-degradable conventional polypropylene plastic that is responsible for several environmental problems at global level. The present study focuses on the isolation and characterization of potential novel PHB producers. A total of 194 PHB producing bacteria were isolated from rhizospheric soils of three different crops; and subjected to microscopic, biochemical and molecular characterization studies. Quantification of PHB production exhibited significant amount of PHB accumulation (120–132 mg/ml) by a few isolates (KW-4, MS-6, RoW-1, AW-1 and RoS-4). Fourier transform infrared spectroscopy (FTIR) analysis of PHB extracted from the isolates was found to be comparable to the spectra of standard PHB, thus, establishing the chemical nature of the extracted polymer. Detection of PHB granules by transmission electron microscopy (TEM) confirmed the isolates to be efficient PHB producers. For molecular characterization of the isolates, phbC gene amplification studies were undertaken, which resulted into an amplification product of 1089 bp, representing the complete PHB synthase gene. Using 16S rRNA gene amplification and sequencing, the selected isolates were identified as belonging to four major genera, viz., Bacillus, Lysinibacillus, Clostridium and Klebsiella; however, Bacillus being the predominant genera. Analysis of 16S rDNA sequences showed that a few isolates exhibited significant differences from their nearest neighbours (similarity level ranging between 93 and 99%), thus, strongly suggesting that they might represent novel strains. Some of these probable novel high PHB producers reported in the present study hold high potential to be exploited for further industrial-scale mass production of biodegradable polymer.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Methodology to determine printability criteria of highly concentrated pastes through rheological characterization

Material extrusion is an additive manufacturing technique that enables the creation of reproducible and complex hardware by depositing a viscous, shear-thinning ink onto a substrate in a custom-pattern via extrusion through a syringe. Here, the ability of an ink to be extruded onto a substrate in many layers, and maintain the desired shape is what defines the printability. Printability is often investigated by formulating, printing, and postmortem analysis of final parts in an iterative manner. Investigations of printability through rheological characterization have often been concerned with inks that straddle the line between printable and too thin, leaving out an entire class of inks that are highly-filled pastes, where extrudability is the limiting factor. Highly-filled pastes continue to pose issues for researchers as the effect of filler morphology, size, loading, and packing fraction on the ink rheology and corresponding printability is not understood. While traditional rheological characterizations may be useful for some inks, we show that protocols utilizing steady-shear, or large-amplitude oscillatory shear are difficult and unreliable for highly-filled pastes. Through transient rheology paired with real-time images we show that each traditional protocol produces inhomogeneous deformations that violate the assumptions that underly common rheological definitions. Instead, we demonstrate metrics measured with small-amplitude oscillatory shear that are correlated to the printability of various ink formulations ranging in loading. The rheological measures that accurately predict the printability of the inks are the axial stress measured at small amplitudes, and the critical stress amplitude above which rheological characterizations become impossible. In addition, we estimate the maximum packing fraction for each filler, based on the exponent common to hard sphere models, and show that the printability of each ink can be predicted by the ratio of the packing fraction to the theoretical maximum. We show how small-amplitude oscillatory shear allows users to develop printability criteria for any ink to enhance the workflow in the development of new inks, increase the performance of material extrusion printing, and improve the stability of printed parts, with less wasted time and materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A streamlined approach to characterize microalgae strains for biomass productivity under dynamic climate simulation conditions

Screening microalgae strains under static light and temperature flask conditions cannot directly quantify the biomass productivities of algae growing in the dynamically fluctuating light and temperature conditions of outdoor ponds. In this effort, we describe a testing pipeline that screens for productivity under climate simulated conditions. A validated, miniaturized Laboratory Environmental Algae Pond Simulator (mini-LEAPS) photobioreactor was used to determine optimal medium salinities and biomass productivities of cold-tolerant microalgae collected from Arctic, subarctic, Antarctic, and subalpine habitats. Strains characterized in this effort include: Chlorella antarctica UTEX1959, Chlorella sp. UTEXSNO69, Chloromonas rosae UTEXSNO11, Phaeodactylum tricornutum UTEX646, and Stichococcus minutus CCALA727. Observed productivities of the characterized strains ranged from 1.74 ± 0.25 to 8.18 ± 0.81 g m –2 day –1 (as ash-free dry weight). For each strain, a temperature tolerance profile was generated to identify the most appropriate season (s) for cultivation. The two most promising strains, Chlorella sp. UTEX SNO69 and P. tricornutum UTEX646, were tested alongside the DISCOVR winter benchmark strain Monoraphidium minutum 26B-AM in outdoor open ponds at the PNNL Algae Testbed (PAT) in Arizona. Further, under cold-season outdoor pond conditions, Chlorella sp. UTEXSNO69 achieved average areal biomass productivities of 8.21 ± 0.50 g m –2 day –1 , not significantly different from that of the benchmark strain (8.52 ± 0.43 g m –2 day –1 , p > 0.05). Under spring outdoor pond conditions, P. tricornutum UTEX646 achieved average areal biomass productivities 10.34 ± 0.27 g m –2 day –1 , not significantly different from that of the benchmark (10.07 ± 0.50 g m –2 day –1 ) under the tested conditions (p > 0.05). The streamlined pipeline was thus demonstrated to successfully characterize productive microalgae strains for outdoor deployment by weeding out algae strains that were not productive under the tested dynamic light and temperature conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Characterizing patterns and variability of building electric load profiles in time and frequency domains

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. Furthermore, the commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

24 POWER TRANSMISSION AND DISTRIBUTION↗