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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Predicted Versus Actual Savings for a Low-Rise Multifamily Retrofit in Boulder, Colorado

To determine the most cost-effective methods of improving buildings, accurate analysis and prediction of the energy use of existing buildings is essential. However, multiple studies confirm that analysis methods tend to over-predict energy use in poorly insulated, leaky homes and thus, the savings associated with improving those homes. In this project, the Building America CARB team evaluated the retrofit of a multifamily building in Boulder, CO. The updated property is a 37 unit, 2 story apartment complex built in 1950, which underwent renovations in early 2009 to bring it into compliance with Boulder, CO's SmartRegs ordinance. Goals of the study were to: 1) evaluate predicted versus actual savings due to the improvements, 2) identify areas where the modeling assumptions may need to be changed, and 3) determine common changes made by renters that would negatively impact energy savings. Other issues that were investigated include the effects of improving building efficiency on tenant comfort, the impact on tenant turnover rates, and the potential market barriers for this type of community scale project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Addressing the Split Incentive Challenge for Enhanced Solar Adoption in Multifamily Rental Properties [Abstract]

The split incentive problem is particularly pronounced in rental markets, where landlords prioritize investments that directly increase property value or rental income. Since energy savings from solar photovoltaic (PV) systems primarily benefit tenants, landlords may perceive little return on investment unless mechanisms exist to recapture some of the financial gains. The primary objective of this project is to develop a publicly available, web-based tool to analyze the U.S. Department of Energy’s ResStock database, which models the U.S. residential building stock. The tool allows users to filter buildings by location, type, HVAC system, square footage, and other characteristics, and outputs typical electric load profiles. By leveraging location-specific electric load data, Fram Energy aims to advance business strategies that address the split incentive barrier and promote the adoption of solar PV installations in rental properties. In addition, a machine learning model will be developed to weigh the marginal contribution of building features across the dataset in predicting electricity demand, supporting guided decision making in forecasting electric load profiles. Lastly, based on each building’s location, load profile, and utility’s electricity rate, an optimized solar photovoltaic array and battery energy storage system will be sized to provide energy arbitrage opportunities.

14 SOLAR ENERGY↗
DOE OSTI2012

Strategy Guideline: Mitigation of Retrofit Risk Factors

The Alliance for Residential Building Innovation (ARBI) Building America team is currently developing strategies designed to promote and achieve increased energy savings and promote upgrades in the residential retrofit sector. These strategies are targeted to retrofit program managers, retrofit contractors, policy makers, academic researchers, and non-governmental organizations. This report focuses on four key areas to promote home energy upgrades: fostering accurate energy savings projections; understanding consumer perceptions for energy savings; measuring energy savings, and ensuring quality control for retrofit installations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Building Large-Scale U.S. Synthetic Electric Distribution System Models

Rapid increases in distributed energy resources on distribution systems are prompting research efforts to improve and evaluate electric power distribution algorithms; however, there is a shortage of realistic, large-scale, U.S.-style test systems for the evaluation of such advanced algorithms. Some available tools to build large-scale test systems are of European style, and their application to studies focused on the United States might not be desirable given very different characteristics between the European and U.S. distribution designs. Motivated by this need, this paper develops detailed algorithms to build large-scale U.S. distribution systems and incorporates them in a new Reference Network Model, RNM-US. The approach starts with information from street maps and a catalog with electric equipment that includes power lines, transformers, voltage regulators, capacitors, and switching devices. The paper presents the algorithms through an illustrative case study of the different steps that comprise the process of building a synthetic distribution grid. Finally, the paper presents a medium- and a large-scale data set covering 10 million electrical nodes and 120,000 km of power lines, demonstrating the applicability of the proposed method to build very large-scale synthetic distribution systems.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Emerging Technologies - Capturing Innovation with Technology

ET team research results are critical to achieving 50% energy savings across U.S. buildings within the next two decades. The ET team focuses on supporting research, development, and tech-to-market opportunities of high impact technologies, or those that demonstrate potential for achieving significant energy savings cost effectively.

Solid State Lighting, Water Heating, Thermal Envel↗
DOE OSTI2024

Mapping use cases and dataset needs for benchmarking buildings data

A perennial challenge in buildings research is the lack of high-quality datasets that can be relied upon for a wide array of tasks, including model calibration and improving energy efficiency and load flexibility. Instrumenting a building for data collection is resource intensive, so it is important to be methodical in the approach and ensure that resulting data are flexible and useful for a broad range of analyses. This study aims to fill the gaps in characterizing potential use cases for buildings datasets and mapping them to dataset needs using a well-defined data infrastructure. Here, we have developed a systematic mapping strategy between buildings dataset needs and use cases to help streamline the processes of efficiently targeting datasets, designing building sensing systems, and determining buildings research use cases. We selected 14 prospective use cases and 11 refined buildings data categories for developing the preliminary dataset-needs-to-use-cases mapping matrix (‘DN-UC mapping matrix’) with generic ‘Tags’—a detailed sub-level of data categories extracted by justifying the needs of an aspect of the datasets to use cases. We present two example applications of the developed mapping matrix to demonstrate use of the mapping matrix and its effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Modelling of air handling unit subsystem in a commercial building

To improve energy efficiency of the Air Handling Unit (AHU), there is a need for a system to monitor energy performance of the AHU in real time, to optimize operation for minimum energy consumption and to calculate energy saving. A mechanical model was created to analyze the performance of subsystems in an AHU and was validated using actual data collected from AHU operation via a wireless monitoring system. Algorithms have been developed to identify malfunctions and estimate the savings if malfunctions are corrected. Here, the model detects control related malfunctions, such as simultaneously turning on the cooling coil and the pre-heating coil. According to the mechanical model of the case study system, a total of 63,455 kWh are wasted within the cooling coil and the pre-heating coil due to this type of control malfunction. Furthermore, the mechanical model can identify other energy savings opportunities due to set point changes; in the case study system, this feature revealed a total of 77,141 kWh energy saving during the same study period. The design, integration, and validation of the mechanical model are described, analyzed, and justified in this paper.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Ontologies at Work: Analyzing Information Requirements for Model Predictive Control in Buildings

Model Predictive Control (MPC) has shown significant potential for improving energy efficiency, indoor air quality and occupant comfort of buildings. MPC-based control algorithms have also shown the ability to shift loads and optimize for multiple objectives, including but not limited to reducing the green-house gas emissions, energy costs and peak demand. However, one of the main implementation challenges of these control algorithms is the integration and configuration effort needed to deploy a supervisory MPC controller in a building. By assigning standardized references to information sources and control points in buildings, existing studies have shown that semantic ontologies and corresponding queries have the potential to ease the deployment of such controllers. Yet, the use of semantic information to ease the deployment processes of MPC controllers is still limited. In this paper, we review three MPC experiments and synthesize the information requirements of these optimization problems. We then turn to existing and upcoming semantic ontologies such as Brick, SAREF and ASHRAE Standard 223 to represent these requirements, evaluating their potential to support the implementation of an MPC controller. This investigation concludes with a discussion of existing opportunities and open questions that the community should explore to support more streamlined MPC implementations.

Prakash, Anand Krishnan↗
DOE OSTI2021

Building Efficiency Targeting Tool for Energy Retrofits (BETTER) Web Application (BETTER Web App) v1.0

The Building Efficiency Targeting Tool for Energy Retrofits (BETTER) V.1.0 web application identifies cost-saving energy and emissions reductions in buildings and portfolios, without site visits or complex modeling. With minimal data entry, BETTER benchmarks a building's or portfolio's energy use against peers; quantifies energy, cost, and greenhouse gas (GHG) reduction potential; and recommends energy efficiency measures for individual buildings or portfolios, targeting specific energy savings levels. The source code of BETTER's modular, cross-platform analytical engine has previously been disclosed (2019-001) and is available on GitHub and can be adopted, redeveloped, and redistributed freely under an open-source license, allowing users to incorporate BETTER's analytical capabilities into their own software platforms and tools. The BETTER V.1.0 web application, developed with the Django web-framework and the Model-View-Controller (MVC) architecture being disclosed here, provides a graphical user interface (GUI) for any user to view the software tutorials, download and upload a data entry template, configure and run the BETTER analyses, and view the final analytical reports.

Szum, Carolyn↗
DOE OSTI2018

Testing and demonstration of model predictive control applied to a radiant slab cooling system in a building test facility

Radiant slab systems have the potential to significantly reduce energy consumption in buildings. However, control of radiant slab systems is challenging. Classical feedback control is inadequate due to the large thermal inertia of the systems and heuristic feed-forward control often leads to unacceptable indoor comfort and may not achieve the full energy savings potential. Model predictive control (MPC) is now attracting increasing interest in the building industry and holds promise for radiant systems. However, an often-cited barrier to its implementation in the building industry is the high computational cost and complexity relative to the feedback controls used in conventional systems. The objectives of this study were to (i) verify the correct operation of an open source MPC toolchain developed for radiant slab systems, and (ii) demonstrate its efficacy in a test facility. A matched pair of cells in the FLEXLAB building test facility at the Lawrence Berkeley National Laboratory was used in the study. The proposed MPC toolchain was implemented in one cell and the performance compared to that of the other cell, which used a conventional heuristic control strategy. Finally, the results showed that the simplified MPC approach applied in the toolchain worked as expected and realized energy savings over the conventional control strategy. The MPC yielded 42% chilled water pump power reduction and 16% cooling thermal energy savings, while maintaining equal or better indoor comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Experimental Design and Laboratory Characterization of a Medium- and High-Efficiency Rooftop Unit for use in Building Energy Simulations

Commercial rooftop units that incorporate variable speed components are quickly entering into the market place; however, the design and modeling tools that calculate the energy benefits of these units cannot properly estimate performance. This is due to two factors: 1) There are insufficient measured performance data of real systems that can be entered into these simulations, and 2) building energy simulators are unable to properly interpret performance data. Therefore, publicly available datasets designed to encompass the entire performance map coupled with control sequence information are needed as inputs for these modelling programs to accurately estimate energy savings and promote variable speed RTUs. As part of the Verification and Uncertainty Characterization for Energy Simulation milestone funded by the Department of Energy, the National Renewable Energy Laboratory recorded and published data across the entire performance range for two commercially available RTUs; one with a two-stage compressor and variable speed supply fan, and another with a fully variable speed compressor, supply fan and condenser fan. This paper details 1) how the design of experiments was created as well as how the units were controlled, and 2) the operational limitations of the two units. The paper will also discuss methods for building energy simulation programs to properly interpret these data sets and integrate them into building energy simulation models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Efficient Computation Of Surface Sunlit Fractions In Urban-Scale Building Modeling Using Ray-Tracing Techniques

For building energy simulation at an urban-scale, solar shading calculations can be significantly slow when a large number of shading surfaces are considered in the solar shading calculations, due to the computational complexity of the geometry calculations. We developed a new algorithm using the ray-tracing technique to pre-calculate the sunlit fractions of all exterior surfaces in an urban district altogether. The ray tracing-based calculator is accelerated using General Purpose Graphics Processing Units (GPGPUs) and the Optix ray tracing library, and provides an efficient, flexible, and robust means for computing the sunlit fraction of large numbers of urban surfaces of complex geometries.

Luo, Xuan↗
DOE OSTI2016

Strategy Guideline. Modeling Enclosure Design in Above-Grade Walls

The Strategy Guideline, written by the U.S. Department of Energy's research team Building Science Corporation, 1) describes how to model and interpret results of models for above-grade walls, and 2) analyzes the failure thresholds and criteria for above-grade walls. A library of above-grade walls with historically successful performance was used to calibrate WUFI (Wärme und Feuchte instationär) software models. The information is generalized for application to a broad population of houses within the limits of existing experience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Construction of Energy-Stable Galerkin Reduced Order Models

This report aims to unify several approaches for building stable projection-based reduced order models (ROMs). Attention is focused on linear time-invariant (LTI) systems. The model reduction procedure consists of two steps: the computation of a reduced basis, and the projection of the governing partial differential equations (PDEs) onto this reduced basis. Two kinds of reduced bases are considered: the proper orthog onal decomposition (POD) basis and the balanced truncation basis. The projection step of the model reduction can be done in two ways: via continuous projection or via discrete projection. First, an approach for building energy-stable Galerkin ROMs for linear hyperbolic or incompletely parabolic systems of PDEs using continuous projec tion is proposed. The idea is to apply to the set of PDEs a transformation induced by the Lyapunov function for the system, and to build the ROM in the transformed variables. The resulting ROM will be energy-stable for any choice of reduced basis. It is shown that, for many PDE systems, the desired transformation is induced by a special weighted L 2 inner product, termed the “symmetry inner product”. Atten tion is then turned to building energy-stable ROMs via discrete projection. A discrete counterpart of the continuous symmetry inner product, a weighted L 2 inner product termed the “Lyapunov inner product”, is derived. The weighting matrix that defines the Lyapunov inner product can be computed in a black-box fashion for a stable LTI system arising from the discretization of a system of PDEs in space. It is shown that a ROM constructed via discrete projection using the Lyapunov inner product will be energy-stable for any choice of reduced basis. Connections between the Lyapunov inner product and the inner product induced by the balanced truncation algorithm are made. Comparisons are also made between the symmetry inner product and the Lyapunov inner product. The performance of ROMs constructed using these inner products is evaluated on several benchmark test cases.

Kalashnikova, Irina↗
DOE OSTI2014

Analysis of the Chinese Market for Building Energy Efficiency

China will account for about half of the new construction globally in the coming decade. Its floorspace doubled from 1996 to 2011, and Chinese rural buildings alone have as much floorspace as all of U.S. residential buildings. Building energy consumption has also grown, increasing by over 40% since 1990. To curb building energy demand, the Chinese government has launched a series of policies and programs. Combined, this growth in buildings and renovations, along with the policies to promote green buildings, are creating a large market for energy efficiency products and services. This report assesses the impact of China’s policies on building energy efficiency and on the market for energy efficiency in the future. The first chapter of this report introduces the trends in China, drawing on both historical analysis, and detailed modeling of the drivers behind changes in floorspace and building energy demand such as economic and population growth, urbanization, policy. The analysis describes the trends by region, building type and energy service. The second chapter discusses China’s policies to promote green buildings. China began developing building energy codes in the 1980s. Over time, the central government has increased the stringency of the code requirements and the extent of enforcement. The codes are mandatory in all new buildings and major renovations in China’s cities, and they have been a driving force behind the expansion of China’s markets for insulation, efficient windows, and other green building materials. China also has several other important policies to encourage efficient buildings, including the Three-Star Rating System (somewhat akin to LEED), financial incentives tied to efficiency, appliance standards, a phasing out of incandescent bulbs and promotion of efficient lighting, and several policies to encourage retrofits in existing buildings. In the third chapter, we take “deep dives” into the trends affecting key building components. This chapter examines insulation in walls and roofs; efficient windows and doors; heating, air conditioning and controls; and lighting. These markets have seen significant growth because of the strength of the construction sector but also the specific policies that require and promote efficient building components. At the same time, as requirements have become more stringent, there has been fierce competition, and quality has at time suffered, which in turn has created additional challenges. Next we examine existing buildings in chapter four. China has many Soviet-style, inefficient buildings built before stringent requirements for efficiency were more widely enforced. As a result, there are several specific market opportunities related to retrofits. These fall into two or three categories. First, China now has a code for retrofitting residential buildings in the north. Local governments have targets of the number of buildings they must retrofit each year, and they help finance the changes. The requirements focus on insulation, windows, and heat distribution. Second, the Chinese government recently decided to increase the scale of its retrofits of government and state-owned buildings. It hopes to achieve large scale changes through energy service contracts, which creates an opportunity for energy service companies. Third, there is also a small but growing trend to apply energy service contracts to large commercial and residential buildings. This report assesses the impacts of China’s policies on building energy efficiency. By examining the existing literature and interviewing stakeholders from the public, academic, and private sectors, the report seeks to offer an in-depth insights of the opportunities and barriers for major market segments related to building energy efficiency. The report also discusses trends in building energy use, policies promoting building energy efficiency, and energy performance contracting for public building retrofits.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

NREL RSF Measured Data 2011

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance. This submission includes the Measured Energy Data from the RSF Systems Model. The data includes a csv with measured data from NREL's Research and Support Facility (RSF) for 2011. The dataset contains hourly data for: Total Cooling (kW)- Total Heating (kW)- Total Mechanical (kW)- Total Lighting (kW)- Total Plug Loads (kW)- Total Data Center (kW)- Total Building (kW)- PV (kW)- Building Net (kW). Energy Model data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

2011↗
DOE OSTI2020

Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning

It has been shown that model predictive control (MPC) is a promising solution for energy-efficient building operations. However, the deployment of MPC in a large portion of the building stock has not been possible partially because of high installation costs. Every building is unique and requires a tailored MPC solution. The best performing solutions are often based on physics-based modeling, which is, however, computationally expensive and requires dedicated software. A promising direction that tackles this problem is to train a neural network-based optimal control policy to imitate the behavior of physics-based MPC from the simulation data generated offline. The neural networks give control actions that closely approximate those produced by physics-based MPC, but with a fraction of the computational and memory requirements and without the need for licensed software. The main advantage of the proposed approach stems from simple evaluation at execution time, leading to low computational footprints and easy deployment on embedded HW platforms. In the case study, we present the energy savings potential of physics-based MPC applied to an office building in Belgium. We demonstrate how neural network approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the remaining challenges and open research questions.

Drgona, Jan↗