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DOE OSTI2022

Co-simulation Framework for Community-scale Building-grid Integration [SWR-21-75]

Distributed energy resources (DERs), including rooftop solar, energy storage, and flexible loads, are gaining popularity as costs decline and as building owners and utilities realize their benefits. DERs can improve distribution system efficiency, help prevent the need for expensive grid upgrades, and increase the resilience of local communities. However, they can also cause difficulties in grid operations and can require controls to achieve their benefits. To address this challenge, NREL researchers have developed a community-scale solution that assesses the impacts of DERs and their control strategies on a distribution system. The framework has been shown to reduce solar photovoltaic (PV) curtailment to 0%, mitigate the adverse impact of solar variability on the distribution voltage, and provide up to 5-day critical load support during emergency events. Utilizing 5 different modules representing the feeder, buildings, home energy management systems, an aggregator, and a utility controller, NREL expects this simulation technology to play a critical role in the continued integration of DERs. According to the Energy Information Administration (EIA), solar curtailments accounted for 94% of the total energy curtailed in the California Independent System Operator (CAISO) in 2020. By enabling Independent System Operators (ISOs) and utility operators to bring solar curtailments to 0%, the electrical grid can become less dependent on fossil-fueled power generation sources. NREL's co-simulation framework contains five major components: Distribution Feeder Model: describes the distribution feeder topology using OpenDSS, including the locations of all DERs. Residential Building Model: simulates a large number of buildings at a high resolution using OCHRETM. The model is equipped to control equipment based on signals from an external module. The model includes major household appliances such as HVAC and a water heater, non-dispatchable load models, a distributed PV system, and a home battery system. Home Energy Management System: optimizes the controls for the devices in a home using foreseeTM. The control can adjust based on the user preferences including cost, comfort, and convenience. In hierarchical control scenarios, where the houses follow signals from an aggregator, the home energy management system provides a flexibility band with a range of power and follows the dispatch signals received from aggregator. Community-Level Aggregator: solves for optimal energy dispatch based on the flexibility bands received from each home and the grid service signal received from the utility controller. Utility-Level Controller: provides grid signals for voltage control using Distributed Energy Resources (DERs), such as solar systems, in the community.

Balamurugan, Sivasathya Pradha↗

DOE OSTI2023

A Simulation Framework for Analyzing the Impact of Stochastic Occupant Behaviors on Demand Flexibility in Typical Commercial Buildings

As one of the primary users of the electric grid, buildings and building equipment, including heating, ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to achieve demand flexibility are to reduce electricity use during peak or critical periods by shutting down equipment or relaxing system setpoints, which will inevitably impact the occupants’ comfort. When occupants feel uncomfortable, they may take actions to regain their comfort, and some of those actions (such as turning on a personal fan) may have a negative impact on meeting the demand response goal. Therefore, it is important to incorporate occupant behaviors into the assessment ofthe building demand flexibility potential. In this study, a simulation framework that includes simulation of zone thermal loads, an HVAC system, and occupant behaviors, was developed to investigate the impact of occupant behaviors on demand flexibility. A case study was conducted using a small office model from the U.S. Department of Energy (DOE) Commercial Prototype Building Models to simulate the building envelope and zone loads. An agent-based occupant thermal behavior model was adapted to forecast occupants’ thermal comfort and their resulting thermal behaviors. An artificial neural network (ANN) based airflow model trained from a computational fluid dynamics (CFD) model of the zone was adopted to better predict the ambient environment of each occupant. An air-source heat pump simulation model that was calibrated from a real two-stage air-source heat pump system was used as the HVAC system. A typical load shedding event during peak hours was studied. Repeated simulations were conducted to capture the stochastic effects of occupant behaviors. The interplay between the demand flexibility, occupant comfort and behavior were analyzed by evaluating key performance indicators, including the energy use, occupant discomfort duration, and occupant behavior duration during the peak period. The results suggest that this framework can be used to analyze typical commercial buildings and their HVAC systems in terms ofdemand flexibility potential under the impact of occupant behaviors.

Chen, Zhelun↗

DOE OSTI2020

City-Scale Building Anthropogenic Heating during Heat Waves

More frequent and longer duration heat waves have been observed worldwide and are recognized as a serious threat to human health and the stability of electrical grids. Past studies have identified a positive feedback between heat waves and urban heat island effects. Anthropogenic heat emissions from buildings have a crucial impact on the urban environment, and hence it is critical to understand the interactive effects of urban microclimate and building heat emissions in terms of the urban energy balance. Here we developed a coupled-simulation approach to quantify these effects, mapping urban environmental data generated by the mesoscale Weather Research and Forecasting (WRF) coupled to Urban Canopy Model (UCM) to urban building energy models (UBEM). We conducted a case study in the city of Los Angeles, California, during a five-day heat wave event in September 2009. We analyzed the surge in city-scale building heat emission and energy use during the extreme heat event. We first simulated the urban microclimate at a high resolution (500 m by 500 m) using WRF-UCM. We then generated grid-level building heat emission profiles and aggregated them using prototype building energy models informed by spatially disaggregated urban land use and urban building density data. The spatial patterns of anthropogenic heat discharge from the building sector were analyzed, and the quantitative relationship with weather conditions and urban land-use dynamics were assessed at the grid level. The simulation results indicate that the dispersion of anthropogenic heat from urban buildings to the urban environment increases by up to 20% on average and varies significantly, both in time and space, during the heat wave event. The heat dispersion from the air-conditioning heat rejection contributes most (86.5%) of the total waste heat from the buildings to the urban environment. We also found that the waste heat discharge in inland, dense urban districts is more sensitive to extreme events than it is in coastal or suburban areas. The generated anthropogenic heat profiles can be used in urban microclimate models to provide a more accurate estimation of urban air temperature rises during heat waves.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Enhancing EnergyPlus capabilities to model dynamic building envelopes using python plugin

Nearly half of the energy consumption in the United States is related to buildings, resulting in an urgent need to develop innovative technologies to improve building energy efficiency. Dynamic building envelopes, comprising switchable insulation and thermal energy storage materials, have been proposed recently as a promising solution to reduce buildings' heating and cooling loads by thermally coupling the indoor environment with the ambient environment when beneficial while decoupling them when outdoor conditions are not favorable. Although various related technologies are still underway, the whole-building energy modeling tools, like EnergyPlus, do not have the capability to simulate the transient and dynamic nature of dynamic envelope materials and components to accurately predict their impact on building energy use. The objective of this study is to formulate a method in EnergyPlus simulation engine to model multilayer envelopes, comprising dynamic building materials with variable thermophysical properties, and discuss the changes made to the program using a Python plugin. Furthermore, the thermal performance of the dynamic envelopes using the proposed method is compared and verified with the results from a well-established commercial code, COMSOL Multiphysics. A parametric assessment is also conducted to evaluate the energy efficiency benefits of dynamic envelopes in a single-family residential building, demonstrating total annual energy savings up to 11.6 %, when a dynamic envelope operates alone, and up to 18.2 % when it is combined with a thin layer of phase change material as a thermal storage medium. Finally, a United States wide energy efficiency assessment is presented to showcase the geographical spread of the energy savings. The method designed and implemented in this study provides the researchers with the ability to implement their dynamic insulation methods in EnergyPlus and evaluate the whole building energy impact.

25 ENERGY STORAGE↗

DOE OSTI2023

Influence of building heat distribution temperatures on the energy performance and sizing of 5th generation district heating and cooling networks

This paper investigates the energy performance and sizing criteria of 5th generation district heating and cooling (5GDHC) networks as a function of the heat distribution temperature in the building systems connected to the district network. An energy simulation model of a 5GDHC network was developed in Modelica for a case study located in Denmark. Calculations were carried out for four different building heating systems. Simulation results show that reducing the heat distribution temperatures from 70 °C to 23 °C leads to around 40% annual electric energy savings (from 10.4 kWh/m 2 to 6.2 kWh/m 2 ) for the operation of the heat pumps. Heat distribution temperatures of 23 °C cause higher water mass flow rates through the network, leading to annual electric energy consumption for the circulation pumps that are almost doubled (from 0.16 kWh/m 2 to 0.3 kWh/m 2 ) compared to the reference case at 70 °C. Furthermore, the paper discusses how the results obtained from the Danish case study can be generalized and applied to other cases using a simplified mathematical approach. In conclusion, it is found that about 1.5% of electric energy savings can be achieved for each temperature degree reduction in the heat distribution system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling

The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytics goals in this domain.

Kandakatla, Akshith R.↗

DOE OSTI2021

Assessing the Energy Resilience of Office Buildings: Development and Testing of a Simplified Metric for Real Estate Stakeholders

Increasing concern over higher frequency extreme weather events is driving a push towards a more resilient built environment. In recent years there has been growing interest in understanding how to evaluate, measure, and improve building energy resilience, i.e., the ability of a building to provide energy-related services in the event of a local or regional power outage. In addition to human health and safety, many stakeholders are keenly interested in the ability of a building to allow continuity of operations and minimize business disruption. Office buildings are subject to significant economic losses when building operations are disrupted due to a power outage. We propose “occupant hours lost” (OHL) as a means to measure the business productivity lost as the result of a power outage in office buildings. OHL is determined based on indoor conditions in each space for each hour during a power outage, and then aggregated spatially and temporally to determine the whole building OHL. We used quasi-Monte Carlo parametric energy simulations to demonstrate how the OHL metric varies due to different building characteristics across different climate zones and seasons. The simulation dataset was then used to develop simple regression models for assessing the impact of ten key building characteristics on OHL. The most impactful were window-to-wall ratio and window characteristics. The regression models show promise as a simple means to assess and screen for resilience using basic building characteristics, especially for non-critical facilities where it may not be viable to conduct detailed engineering analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗

DOE OSTI2020

Assessing uncertainty in the timing of energy use during cost-optimal distributed energy technology selection and sizing

This paper empirically derives uncertainty ranges in cost-optimal solar PV and storage sizing by comparing results from the REopt Lite optimization platform from metered data and a set of simulated Department of Energy Commercial Reference Building (CRB) profiles at 65 sites. We find load profile shape alone does not explain a site’s optimal configurations (i.e., PV, Storage, PV and Storage, No System). Still, load profile shape does introduce uncertainty to optimal PV and storage capacities. Across all cases where PV is part of an optimal configuration, we find the average ratio of power capacities derived from metered loads to capacities derived from CRB profiles to be 0.97 (and as high as 1463), where 1 would be a perfect match in system size. For storage, the ratio is 1.6 (and as high as 42). We also assess how, in the absence of complete metered data, a CRB profile can be selected that would be expected to yield the most similar solar PV and storage capacities. From those metrics that can be available from billing data (i.e., peak demand, monthly load totals), we find that uncertainty is most reduced by selecting the CRB’s with an annual peak occurring at the most similar time, or those with the lowest average root mean square error (RMSE) among monthly peak loads. This research can help improve the implementation and interpretation of results derived from simulated load profiles and is an important next step in advancing smart grid solutions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Evaluating the performance of WRF urban schemes and PBL schemes over Dallas-Fort Worth during a dry summer and a wet summer

This study evaluated the Weather Research and Forecasting (WRF) model sensitivity to different planetary boundary layer (PBL) schemes (the YSU and MYJ schemes) and urban schemes including the bulk scheme (BULK), single-layer urban canopy model (UCM), multi-layer building environment parameterization (BEP) model, and multi-layer building energy model (BEM). Daily reinitialization simulations were conducted over Dallas-Fort Worth during a dry summer month (July 2011) and a wet summer month (July 2015) with weaker (stronger) daytime (nocturnal) UHI in 2011 than 2015. All urban schemes overestimated the urban daytime 2m temperature in both summers, but BEP and BEM still reproduced the daytime urban cool island in dry summer. All urban schemes reproduced the nocturnal urban heat island, with BEP producing the weakest one due to its unrealistic urban cooling. BULK and UCM overestimated the urban canopy wind speed, while BEP and BEM underestimated it. The urban schemes showed prominent impact on daytime PBL profiles. UCM+MYJ showed a superior performance than other configurations. The relatively large (small) aspect ratio between building height and road width in UCM (BEM) was responsible for the overprediction (underprediction) of urban canopy temperature. The relatively low (high) building height in UCM (BEM) was responsible for the overprediction (underprediction) of urban canopy wind speed. Improving urban schemes and providing realistic urban parameters were critical for improving urban canopy simulation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

Energy consumption analysis of the Venus Deep Space Station (DSS-13)

This report continues the energy consumption analysis and verification study of the tracking stations of the Goldstone Deep Space Communications Complex, and presents an audit of the Venus Deep Space Station (DSS 13). Due to the non-continuous radioastronomy research and development operations at the station, estimations of energy usage were employed in the energy consumption simulation of both the 9-meter and 26-meter antenna buildings. A 17.9% decrease in station energy consumption was experienced over the 1979-1981 years under study. A comparison of the ECP computer simulations and the station's main watt-hour meter readings showed good agreement.

Hayes, N. V.↗

DOE OSTI2023

Bias Correction in Urban Building Energy Modeling for Chicago Using Machine Learning

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.

Chowdhury, Shovan↗

DOE OSTI2025

Quantifying and Optimizing the Energy Benefits of Mass Timber Construction

The International Mass Timber Alliance (IMTA) is a global organization of industry leaders, engineers, scientists, and associations dedicated to advancing mass timber construction. Its mission is to generate and disseminate scientific data supporting the development of standardized construction and energy efficient practices that promote the adoption of mass timber worldwide. IMTA collaborated with Oak Ridge National Laboratory (ORNL) to leverage ORNL’s expertise in building envelope modeling and testing to evaluate how mass timber construction can reduce peak heating and cooling demand, lower overall energy use, and improve resilience during power outages. A previous study of 80 mass timber buildings in Finland found measured energy use up to 50% lower than predicted by simulation. This project aimed to validate and extend those findings for U.S. buildings through analytical modeling, laboratory testing, and full-scale building evaluations. The research focused on the thermal performance of low-embodied-energy wall assemblies, such as cross-laminated timber (CLT) panels and log walls, with particular attention to the effects of thermal inertia on indoor comfort and energy performance. While mass timber’s structural and fire-resistance properties are well documented, its whole-building thermal behavior has received limited attention. Field data, simulation results, and resilience testing from this study will inform future modeling practices, design guidelines, and construction practices by quantifying the unique thermal and demand-flexibility benefits of mass timber construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

AlphaBuilding - Synthetic Buildings Operation Dataset

This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

AlphaBuilding↗

DOE OSTI2023

ResStock: Annual Baseline Results with Component Loads

The ResStock Analysis Tool was developed by NREL with support from the U.S. Department of Energy to provide a new approach to large-scale residential analysis by combining large public and private data sources, statistical sampling, detailed sub hourly building simulations, and high-performance computing. This combination achieves unprecedented granularity and accuracy in modeling the diversity of the housing stock and the distributional impacts of building technologies in different communities. The annual baseline energy results from a national-scale ResStock run use typical meteorological year 3 (TMY3) files for energy simulations. Results include heating and cooling loads for individual components of each building. Component loads describe the heating/cooling load that can be attributed to specific elements of a home, such as heat transfer through walls or internal gains. Additionally, these results include the standard ResStock outputs for housing characteristics and numerous energy outputs by end-use and fuel. A snapshot of the ResStock version used to produce this data, including a configuration file for the run can be found using the Source Code resource link.

Array↗

NASA NTRS2020

Robotic Specialization in Autonomous Robotic Structural Assembly

Robotic in-space assembly of large space structures is a long-term NASA goal to reduce launch costs and enable larger scale missions. Recently, researchers have proposed using discrete lattice building blocks and co-designed robots to build high-performance, scalable primary structure for various on-orbit and surface applications. These robots would locomote on the lattice and work in teams to build and reconfigure building-blocks into functional structure. However, the most reliable and efficient robotic system architecture, characterized by the number of different robotic 'species' and the allocation of functionality between species, is an open question. To address this problem, we decompose the robotic building-block assembly task into functional primitives and, in simulation, study the performance of the the variety of possible resulting architectures. For a set consisting of five process types (move self, move block, move friend, align bock, fasten block), we describe a method of feature space exploration and ranking based on energy and reliability cost functions. The solution space is enumerated, filtered for unique solutions, and evaluated against energy and reliability cost functions for various simulated build sizes. We find that a 2 species system, dividing the five mentioned process types between one unit cell transport robot and one fastening robot, results in the lowest energy cost system, at some cost to reliability. This system enables fastening functionality to occupy the build front while reducing the need for that functional mass to travel back and forth from a feed station. Because the details of a robot design affect the weighting and final allocation of functionality, a sensitivity analysis was conducted to evaluate the effect of changing mass allocations on architecture performance. Future systems with additional functionalities such as repair, inspection, and others may use this process to analyze and determine alternative robot architectures.

Bernus, Borbala↗

DOE OSTI2022

pnnl/simple-building-calculator

The Simple Building Calculator is a web application that estimates annual energy use using regression models that have been fit to simulation results for the DOE Commercial Prototype Building Models. The tool is a single-page application that will allow a user to rapidly analyze the impact of energy efficiency measures and design options on building performance. As it uses linear regression models rather than more complicated machine learning models or physics based simulation, the tool can deploy easily and run client-side.

Xu, Weili↗

DOE OSTI2023

EUI Benchmarks for Net-Zero Energy Buildings in India

In our study we present EUI benchmarks for NZEBs for six building types across residential and non-residential typologies and for India's five climate zones. This approach is similar to the simulation-based benchmarks used by the B3 program in Minnesota, the Cal-Arch methodology in California, and the US Solar Decathlon approach, which combine simulations with actual building data. Of the six building types we explore one in detail with a range of operation scenarios, specifically narrowing down the EUI benchmarks for mixed-mode building operation and for variable temperature set-points as prescribed in the National Building Code of India.The contribution of this work is to provide rigorous end-use level EUI benchmarks for six building types, and to describe a method for simulation-based EUI benchmarks for mixed-mode operation with variable setpoints to highlight the difference between the standard approach used for the five building types and the On-Site Construction Worker Housing which additionally has the mixed-mode variable setpoint approach.On-Site Construction Worker Housings are typically poorly constructed temporary structures, without adequate thermal comfort, It is critical to provide adequate thermal comfort to protect people from the warming effects of climate change, and to discover super efficient and cost-effective ways to do so.The methodology used for the On-Site Construction Worker Housing (CWH) results in an 80% acceptability according to the India Model for Adaptive Comfort in the National Building Code of India. EUIs of all six buildings are 60% lower than the minimum compliance with India's Energy Conservation Building Codes, providing benchmarks for efficiency levels. The end-use level EUI benchmarks are now provided to over 1800 Solar Decathlon India participants so that they can compare the performance of their NZEB designs.In particular, the CWH results provide an insight into the importance of the mixed-mode operation with variable temperature set-points. The results from the simulation study show that for an NZEB target, the EUI with standard thermal comfort model and without mixed operation is 58.26 kWh/m2*year, while that with the variable set-points of the adaptive model with mixed mode operation is 24.23 kWh/m2*year. This is a 58% reduction in EUI. Given that many building types including residential, and non-residential operate in mixed mode, it is important to take this work further to develop mixed-mode operation NZEB benchmarks so that the carbon intensity of these buildings could be lower than the standard thermal comfort model approach.

benchmarking↗