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DOE OSTI2025

Geomechanical characterization of Rock Valley carbonates

In Rock Valley, Nevada, the USA, past earthquake sequences and shallow faulting in Paleozoic carbonates remain poorly understood. Carbonates are typically more ductile rocks that do not experience large stress drops or fracture coalescence, which contradicts previous observations in the region. As part of the Source Physics Experiment, an effort has been made to experimentally characterize the petrophysical and geomechanical properties of carbonates from Rock Valley. Well core and outcrop samples were used to determine the difference between shallow-buried Tertiary limestones and deeply buried Paleozoic limestones and dolostones. The carbonates possessed porosities between 3 and 9%, with V P and V S in the Tertiary carbonates around 2700 and 1800 m/s, respectively, and 6000 and 3100 m/s, respectively, in the Paleozoic carbonates. Microstructural characterization revealed that the Paleozoic carbonates contained significantly more pre-existing damage and alteration than the Tertiary carbonates, though the deformation varies from localized to diffuse. Unconfined Brazilian tests and triaxial tests with confining pressures between 0 and 50 MPa showed that, although samples all experienced failure, the dolostones typically failed at greater stresses and possessed greater E/ν ratios than the limestones. Furthermore, the Hoek–Brown failure criterion was used to construct failure envelopes with the compressive and tensile failure tests. Velocity, density, and porosity measurements were used to construct a hypothetical velocity-depth profile and compare the results with the theoretical petrophysical measurements. Using brittleness analysis, the likely failure conditions were determined to be low-porosity dolomitic rock for fault nucleation in the Paleozoic basement.

Brittleness↗

DOE OSTI2020

Perspectives on Quenching and Tempering 4340 Steel

Steels are ubiquitous due to their affordability and the landscape of useful properties that can be generated for engineering applications. But to further expand the performance envelope, one must be able to understand and control microstructure development by alloying and processing. In this paper we use multiscale, advanced characterization to better understand the structural and chemical evolution of AISI 4340 steel after quenching and tempering (Q&T), including the role of quench rate and short-time, isothermal tempering below 573 K (300 °C), with an emphasis on carbide formation. We compare the microstructure and/or property changes produced by conventional tempering to those produced by higher temperature, short-time “rapid” tempering. We underscore that no single characterization technique can fully capture the subtle microstructure changes like carbon redistribution, transition carbide and/or cementite formation, and retained austenite decomposition that occur during Q&T. Only the use of multiple techniques begins to unravel these complexities. After controlled fast or slow quenching, η transition carbides clearly exist in the microstructure, likely associated with autotempering of this high martensite start temperature (Ms) steel. Isothermal tempering below 598 K (325 °C) results in the relief of carbon supersaturation in the martensite, primarily by the formation of η transition carbides that exhibit a range of carbon levels, seemingly without substitutional element partitioning between the carbide and matrix phases. Hägg transition carbide is present between 300 °C and 325 °C. After conventional tempering at or above 598 K (325 °C) for 2 h, cementite is predominant, but small amounts of cementite are also present in other conditions, even after quenching. Previous work has indicated that silicon (Si) and substitutional elements partition between the cementite, which initially forms under paraequilibrium conditions, and the matrix. Phosphorous (P) may also be preferentially located at cementite/matrix interfaces after high temperature tempering. Slower quench rates result in greater amounts of retained austenite compared to those after fast quenching, which we attribute to increased austenite stability resulting from “autopartitioning”. Rapid, high temperature tempering is also found to diminish tempered martensite embrittlement (TME) believed to be associated with the extent of austenite decomposition, resulting in mechanical properties not attainable by conventional tempering, which may have important implications with respect to industrial heat treatment processes like induction tempering. Controlling the amount and stability of retained austenite is not only relevant to the properties of Q&T steels, but also next-generation advanced high strength steels (AHSS) with austenite/martensite mixtures.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Design of Mini-Plate-1 Irradiation Test for Qualification of High-Density, Low-Enriched U-10Mo Monolithic Fuel

The United States High Performance Research Reactor project is tasked with fuel development and qualification leading to conversion of higher power research and test reactors in the US from high-enriched uranium (HEU) to low-enriched uranium (LEU) fuels. Here, this manuscript identifies the functional and operational design requirements of the first miniature test plate (mini-plate [MP]) irradiation campaign (MP-1) of commercially fabricated LEU U-10Mo monolithic plate-type fuel and is the precursor to a large parametric mini-plate test (MP-2) aimed at producing the data to support regulatory qualification of the LEU U-10Mo monolithic fuel. The manuscript (1) provides a general description of the selected U-10Mo LEU fuel and (2) defines the overall experiment design and functional requirements to accomplish the specific test objective of MP-1, which is to confirm that the commercially manufactured LEU U-10Mo monolithic fuel meets the established requirements of geometric stability, mechanical integrity and stable and predictable behavior. The fuel testing parameters are established by the need to bound performance behavior within the operational envelope of the reactors being converted.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Timed to save: the added value of accounting for hourly incidence of electricity savings from residential space-conditioning measures

Abstract Previous research has recognized that the value of measures that reduce electricity usage depends upon the timing of the savings generated, but the lack of hourly savings shapes has limited the demonstration of this concept. We develop empirical hourly savings shapes for residential space-conditioning measures from nearly 18,000 efficiency projects in California and show how they combine with the diurnal and seasonal variation in electricity system costs. We find that these measures (cooling replacements; windows, doors, and skylights; and other envelope measures) tend to save electricity when system costs are highest and that the hourly savings account for 1.4–1.5 times as much value as non-time-sensitive estimates of efficiency would predict. We present these impact multipliers for each measure to quantify the additional value revealed by the time-sensitive approach. We show that this additional value is similar in an evolving electricity grid with storage, rather than natural gas generation, as the marginal resource.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Evaluating the energy impact potential of energy efficiency measures for retrofit applications: A case study with U.S. medium office buildings

Quantifying the energy savings of various energy efficiency measures (EEMs) for an energy retrofit project often necessitates an energy audit and detailed whole building energy modeling to evaluate the EEMs; however, this is often cost-prohibitive for small and medium buildings. In order to provide a defined guideline for projects with assumed common baseline characteristics, this paper applies a sensitivity analysis method to evaluate the impact of individual EEMs and groups these into packages to produce deep energy savings for a sample prototype medium office building across 15 climate zones in the United States. We start with one baseline model for each climate zone and nine candidate EEMs with a range of efficiency levels for each EEM. Three energy performance indicators (EPIs) are defined, which are annual electricity use intensity, annual natural gas use intensity, and annual energy cost. Then, a Standard Regression Coefficient (SRC) sensitivity analysis method is applied to determine the sensitivity of each EEM with respect to the three EPIs, and the relative sensitivity of all EEMs are calculated to evaluate their energy impacts. For the selected range of efficiency levels, the results indicate that the EEMs with higher energy impacts (i.e., higher sensitivity) in most climate zones are high-performance windows, reduced interior lighting power, and reduced interior plug and process loads. However, the sensitivity of the EEMs also vary by climate zone and EPI; for example, improved opaque envelope insulation and efficiency of cooling and heating systems are found to have a high energy impact in cold and hot climates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Evaluating indoor thermal resilience of passive and low-power cooling shelters for outdoor workers in India

India’s 231 million outdoor workers are exposed to deadly heat during heat waves. We report results from a simulation-based study that focuses on passive design of cooling shelters for the hot and dry climate zone of India. The goal is to design a cooler-than-outdoor shelter for outdoor workers’ intermittent rest and recovery from heat stress during their arduous outdoor work, to avoid heat-related morbidities and mortality. Expected indoor thermal conditions in low-cost cooling shelters deploying a range of passive designs and low-power active measures are investigated, consistent with the current Indian standards and practices, located in the city of Jodhpur in western India. Heat resilience of the shelter was measured with the predicted reduction of hazardous hours inside the shelter based on the wet-bulb globe temperature (WBGT) and extended heat index. These measures include improving the building envelope, installing cool roofs, using internal thermal mass, and natural ventilation. Additionally, a low-power active measure of using ventilation fans and ceiling fans is explored. EnergyPlus-based simulation results show that simple, commonly available measures, particularly natural ventilation and ceiling fans, achieve the most significant reductions in overheating danger hours, by 22% and 21%, respectively, while more complex or costly passive strategies yield only marginal additional benefits. Moreover, combined measure packages are identified that can reduce the indoor WBGT by 8 to 10 °C, eliminating overheating danger hours. These findings highlight the critical importance of scalable, low-cost, and easily deployable cooling solutions for developing resilient cooling shelters for vulnerable outdoor workers during heat waves in India.

building performance↗

DOE OSTI2024

A high-resolution pseudo-polygon discrete element model for regional sea ice

Here, this work presents a pseudo-polygon discrete element model for high-resolution sea ice simulations. A scale-invariant bonded particle contact model is proposed to model joints between sea ice floes based on the smeared fracture model and a lattice spring beam model, and the Mohr–Coulomb failure criterion is implemented to represent the shearing failure mechanism of sea ice packings under complex loadings. All mechanical parameters of the bond model can be directly determined from laboratory tests. Validations of the proposed model are made by investigations of mechanical response and failure criteria of field sea ice sheets. Compared with the field observations of sea ice from satellite radar and in situ stress sensors, the proposed model is capable of reproducing the typical constitutive behavior and the Coulomb friction envelope of field sea ice. Finally, the proposed discrete element sea ice model is used to study the effect of loading rates on mechanical behavior including failure strength of regional sea ice.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Predicting System Response at Unmeasured Locations

Traditional techniques to derive dynamic specification for components have a great deal of uncertainty. One of the major sources of uncertainty is that the number of response measurements in the operational system environment is insufficient to determine the component motion. This inadequacy is due to logistical limitations for data recording in field testing and space limitations for accelerometers, strain gages and associated wiring. Available measurements are often some distance from the component and therefore do not represent component motion. Typical straight-line envelopes of these unrepresentative measurements guarantee an increase in the uncertainty. In this paper multiple methods are attempted to expand a sparse set of field test measurements on a system to responses of interest that cannot be measured in the field due to the limitations. Proof of concept is demonstrated on the Modal Analysis Test Vehicle (MATV). The responses of interest, known as “truth responses”, are measured in a system vibration environment along with an optimized sparse set of 30 field responses. Methods to expand the field responses to the truth responses are demonstrated by comparing the acceleration spectral density of the expanded response to the measured response. Two methods utilize a validated finite element model of the MATV. One is developed from purely experiment based frequency response functions of a laboratory pre-test. Finally, these approaches are designed to drastically reduce the uncertainty of the component in-service motion as a basis for developing specifications that are guaranteed to be conservative with a known (instead of unknown) conservatism.

42 ENGINEERING↗

DOE OSTI2026

Optimization of the FRIB beam dump: a hybrid genetic algorithm and reinforcement learning approach

The operational envelope of high-power-density systems, such as particle accelerators and advanced nuclear energy systems, is critically constrained by the need to manage extreme thermal loads. To address this, we present a novel hybrid optimization framework combining a genetic algorithm (GA) with a soft actor-critic (SAC) deep reinforcement learning agent. This framework was applied to a practical high-heat-flux problem: redesigning the beam dump at the Facility for Rare Isotope Beams (FRIB) for a power upgrade from 20 kW to 50 kW. The resulting design, validated by three-dimensional conjugate heat transfer simulations, suppresses hazardous hot spots and yields a markedly more uniform temperature distribution. This provides a robust operating margin, increasing the average power-handling capability by 72% relative to the current design, demonstrating the framework’s potential to solve complex thermal management challenges in both accelerator technology and advanced nuclear systems.

Accelerator↗

DOE OSTI2020

Ultrastructure of the green alga Dunaliella salina strain CCAP19/18 (Chlorophyta) as investigated by quick-freeze deep-etch electron microscopy

The single-celled green alga Dunaliella salina is a model system for studies on stress biology, in particular regarding secondary carotenoid accumulation. Under non-stress conditions the cells are green, but under abiotic stress the cells turn orange, because they switch their metabolism and accumulate β-Carotene in globules in the chloroplast. For the first time, Quick-freeze deep-etch electron microscopy was used to visualize cellular structures in green and orange cells of D. salina strain CCAP19/18. This allowed us to present an in-depth analysis of the cellular ultrastructure describing and comparing the features of the two cell types. Our images illustrate the presence of a pericellular matrix for this strain of D. salina. The pericellular matrix was spongy and strands of unknown material anchored it into the plasma membrane. The cytoplasm contained a variety of vesicles, vacuoles, and acidocalcisomes. We could show for strain CCAP19/18 that cytoplasmic lipid bodies were often in close proximity to and sometimes in contact with the outer chloroplast envelope membrane and with the endoplasmic reticulum. Major visible differences between green and orange cells were in the chloroplast: the orange cells have greatly reduced amounts of thylakoid membranes and greatly increased numbers of β-Carotene globules. We showed that the β-Carotene globules often made point contacts with thylakoid membranes, and frequently laid side-by-side along the thylakoid membrane surface, providing support to studies that indicated exchange of molecules between β-Carotene globules and thylakoid membranes. A unique finding was the β-Carotene globule duplets, suggesting intermediate stages in β-Carotene globule morphogenesis. Overall, the β-Carotene globules appear to be similar to plant plastoglobuli. We provide this description of the cellular ultrastructure features as a resource in the context of the recent publication of the genome of D. salina strain CCAP19/18 to expand on the knowledge regarding this novel reference strain.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Reactor and fuel cycle performance of light water reactor fuel with 235 U enrichments above 5%

Recent advances in nuclear fuel materials research, particularly on the topic of accident-tolerant fuels, have brought up potential opportunities for expanding the operating envelope of existing light water reactors. As many of the performance improvements offered by these technologies may be most fully realized by increasing fuel enrichment beyond the standard 5% limit, this paper examines the potential reactor performance and fuel cycle performance of low-enriched uranium oxide fueled light water reactors by generically considering pressurized water reactors with 235 U enrichment from 5 to 7%. Advanced cladding, including accident-tolerant cladding, has the potential to increase fuel burnup limits related to hydrogen in the cladding that coincide with those limits associated with end-of-life reactivity. Therefore, higher enrichment will be necessary in order to realize the higher fuel burnups. This work includes evaluation of the fuel cycle length, discharge burnup, reactivity coefficients, and fuel cycle performance, including radioactive waste and environmental impact metrics per unit energy generated. The analysis was performed using the evaluation metrics from the US Department of Energy Office of Nuclear Energy Fuel Cycle Evaluation and Screening Study. The reactor performance and safety analysis show that enrichments between 5 and 7% would have similar fuel temperature and moderator temperature coefficients. However, the soluble boron coefficient would decrease in magnitude, requiring more corrosive boric acid in the coolant or other methods of reactivity control during the fuel cycle. At these higher enrichments the maximum burnup at the rim of the fuel pellet would increase by almost a factor of two, which is expected to impact the formation of high-burnup structure in the fuel and the corresponding thermo-mechanical fuel properties. The fuel cycle performance assessment shows that increasing enrichment reduces the quantity of high-level waste disposed per unit energy generated, but it increases the natural resource requirements normalized to a gigawatt-electricity-per-year basis. Another impact is the slightly higher discharge burnup, resulting in somewhat different activity levels of the spent nuclear fuel and high-level waste radioactivity at 100 and 100,000 years after fuel discharge. The environmental impacts—including land use, water use, carbon emission, and radiological exposure—are of the same magnitude per unit energy generated. Finally, however, the impacts are distributed differently. Less than 5% enrichment has marginally more impact on the back-end of the fuel cycle, and greater than 5% enrichment has marginally more impact on the front-end of the fuel cycle. Ultimately, no neutronic or reactor safety hindrances to employing light water reactor fuel with enrichments greater than 5% are identified; given the achievable reactor performance benefits with advanced fuels, further practical exploration of increased enrichment fuel is recommended.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Development and validation of a second-order thermal network model for residential buildings

Heating, Ventilation, and Air Conditioning (HVAC) systems can maintain the space air temperature of residential buildings, either directly by heating/cooling the air, or indirectly via heat transfer to and from the building structure that acts as a thermal mass. Hence, HVAC systems can help achieve load shifting, peak load reduction, and/or energy cost saving, thus enabling grid-interactive HVAC operation. A home thermal model that can accurately reflect the dynamics of the space air and interior wall surface temperatures, is therefore valuable. This paper develops such a model using the standard RC (resistance-capacitance) approach. The model contains a virtual envelope node and an internal space node and is thus second-order. A hybrid parameter identification scheme, made up of the least-squares and optimal search methods, is also developed. The proposed model and scheme were validated using data collected from a test home. It was found that a modest amount of training data was sufficient to yield reliable parameter estimates and accurate prediction. It was also found that when making 24-hour-ahead prediction of the space air temperature, both methods had comparable performances when the training data began in a transition season. However, when they began in an HVAC season, the optimal search method performed better. Furthermore, the least-squares method is recommended during a transition season due to its lower computational burden, while the optimal search method is recommended during an HVAC season due to its better estimation performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A hybrid architecture for volt-var control in active distribution grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Finally, our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods

We report home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment. This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗

DOE OSTI2026

Calibration of urban building energy model using smart meter data for district peak load prediction

Urban building energy modeling (UBEM) is a powerful approach to assessing baseline building energy performance and retrofits with new technologies across building stocks in cities. However, the accuracy of UBEM is often constrained by the limited availability of reliable data about building characteristics and operations, such as envelope efficiency levels, HVAC system performance, and end-use load patterns. Existing research has performed UBEM calibration using annual or monthly energy consumption data, which falls short when higher-resolution time series applications are needed, such as peak load prediction for utility operation planning. This study presents a new framework for calibrating building energy models at urban scale using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6 % during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Using the calibrated models, we analyzed the demand flexibility potential of the district building stock as an application of UBEM calibration. The findings affirm the appropriate use of UBEM for peak electric load forecasting and demand side management at the utility distribution system level.

AMI data↗