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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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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Performance evaluation of the Solar Building Test Facility

The general performance of the NASA Solar Building Test Facility (SBTF) and its subsystems and components over a four year operational period is discussed, and data are provided for a typical one year period. The facility consists of a 4645 sq office building modified to accept solar heated water for operation of an absorption air conditioner and a baseboard heating system. An adjoining 1176 sq solar flat plate collector field with a 114 cu tank provides the solar heated water. The solar system provided 57 percent of the energy required for heating and cooling on an annual basis. The average efficiency of the solar collectors was 26 percent over a one year period.

Jensen, R. N.↗
DOE OSTI2022

On-policy learning-based deep reinforcement learning assessment for building control efficiency and stability

Artificial intelligence technologies have emerged as a game changer not only in specific applications such as image recognition and machine translation but also in many scientific domains. In particular, as deep reinforcement learning (DRL) has shown great success in complex control problems, DRL-based control has been considered as a potential solution to efficiently control and manage building systems. However, broad assessment of DRL-based building control is still required to characterize their pros and cons in comparison with conventional building control methods (e.g., rule-based feedback controls). In this paper, we assessed DRL-based controls with on-policy learning-based algorithms and continuous control actions for cooling control of large office buildings in the summer season to minimize whole-building energy use and occupant discomfort. We compared DRL-based control methods with two baseline control methods: (1) a pre-determined schedule with supply temperature and static pressure setpoints, and (2) advanced reset method that adjusts setpoints based on heuristic rules, i.e., ASHRAE Guideline 36. We also tested the DRL algorithms to evaluate their performances in multiple climate locations. We found that DRL-based control methods outperformed the baseline control methods in terms of energy savings while maintaining a thermal comfort. DRL reduced energy use between ~4%–22% on average compared to the baseline methods, depending on climate location. We also evaluated DRL-based control in terms of control stability and showed that DRL-based methods should address the span of hardware lifetimes in practical operations.

control stability↗
DOE OSTI2021

Parametric study of solid-solid translucent phase change materials in building windows

Thermal energy storage and solar radiation management are crucial to improve the sustainability and energy efficiency of buildings. Compared with the implementation of phase change materials (PCMs) in opaque components, the energy saving potential of incorporating PCMs in transparent glazing windows is much less studied and not well understood. Here we present a comprehensive parametric study of novel PCM windows for building energy saving with a focus on optimizing and quantitatively distinguishing the contributions from the optical and thermal properties of the PCM, which is particularly useful for the design of solid-solid PCM windows. We investigate a reference commercial office building using EnergyPlus by developing an equivalent model of our PCM window that is compatible with EnergyPlus's modeling capabilities. Compared with a clear-clear double-pane window, the integration of 3 mm solid-solid PCMs with optimal properties in warm, mixed, and cold climates can respectively save up to 17.2%, 14.0%, and 5.8% energy for the HVAC (heating, ventilation, and air conditioning) system, and 9.4%, 6.7%, and 3.2% energy for the whole building. We also demonstrate that these energy savings are most sensitive to the solar absorptance of PCMs for all three climates. The optimal transition temperature varies with climate and is related to the climate and solar radiation heat gain. Other issues are also briefly discussed, such as hysteresis, window orientations, and the effect of interior lighting. Finally, although the optimal PCM windows show energy saving performance comparable with low-emissivity windows, the PCM windows provide a unique advantage in terms of shifting HVAC loads which can provide benefits to the electrical grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Brick Schema Standardized Plug Load Control Strategies for Load Reduction: Preprint

Plug loads comprise a significant percentage of commercial building energy consumption. Applying intelligent controls to turn off plug loads when unused can provide dynamic load reduction and flexibility, which are key traits of grid-interactive efficient buildings. This capability is important for equitable decarbonization as it can enable disadvantaged communities to electrify buildings without costly upgrades to electrical infrastructure. In this work, we present the effectiveness of various control strategies along with the operational lessons that informed their design. During a three-year period, we operated over 600 smart outlets in 12 university office buildings. The attached plug loads consisted primarily of printers, TVs, water dispensers, and copiers. After recording baseline power measurements for one year, we designed plug load control (PLC) strategies for each plug load type, use, and for different risk tolerance levels because PLC can potentially be disruptive to daily work. We used the Brick Schema to facilitate the management of plug load locations and other metadata. For advanced controls, we integrated the smart plugs with heating, ventilation, and air conditioning (HVAC) systems through the campus building automation system. We found static schedules to be the least disruptive and most predictable for occupants, resulting in 38% and 66% energy savings in two studies. For printers, print server-triggered PLC produced 86% savings, the highest of all strategies with minimal occupant impact. Scheduling of water dispensers and digital signage TVs produced 49% and 70% savings respectively with opportunities to improve performance with the use of HVAC occupancy data.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2024

Developing a Control Strategy for Minimum Airflow Setting Considering CO2 Level and Energy Consumption in a Variable Air Volume System

In an office building equipped with a Variable Air Volume (VAV) system, this paper introduces a novel method for controlling the minimum supply airflow fraction in each zone’s VAV box, having a capability to consider indoor CO2 level and energy consumption. The EnergyPlus simulation using the medium office prototype model was employed, which evaluated the performance of the energy and CO2 concentration for five VAV box airflow control strategies. The paper focuses on CO2 concentration-based airflow control method and compares it with other four methods including conventional single-max, reduced minimum single-max, demand-controlled ventilation(DCV), and dualmax control methods according to guidelines and common practices. The newly proposed control strategy directly correlates the minimum airflow fraction to CO2 concentration. A general trend emerged when comparing CO2 concentrations—lower minimum airflow fractions were associated with higher concentrations. The proposed control method effectively maintained low CO2 concentrations and enabled a lower airflow fraction contributing to energy consumption reduction. It was confirmed that heating energy consumption in climate zone 4A, 5B, and 6A showed a maximum saving of approximately 30% compared to the conventional single-max and dual max control strategies. It was found that cooling energy consumption in climate zone 4A and 6A can achieve a maximum saving of approximately 10% compared to the conventional control strategies. The proposed CO2 concentration-based control logic is promising as it not only improves the indoor air quality lowering the CO2 concentration in the occupied spaces, but also contributes to HVAC energy savings.

Lee, Jong Man↗
DOE OSTI2022

Photovoltaic windows cut energy use and CO 2 emissions by 40% in highly glazed buildings

Buildings account for 30% of global energy use. The architectural trend across building sectors is toward more glass despite higher energy use and carbon emissions than opaque cladding alternatives. Numerous window technologies - low-emissivity coatings, triple glazing, dynamic tinting, and the more recently developed photovoltaic glass - have emerged in the last two decades as approaches to reduce building energy. However, a comprehensive understanding of where and how these window technologies can be installed to enable optimal energy savings under different climate conditions remains limited. Here we test window technologies using thousands of macroscale building-energy simulations for different climate zones and building designs to evaluate the associated net energy use and carbon-emissions reduction potential. Novel window technologies, especially photovoltaic windows with high thermal performance, offer energy savings in all climates, ranging from 10,000-40,000 GJ per year over substandard windows for a typical office building, resulting in up to 2,000 tons of annual CO 2 emissions reduction. Highly glazed, net-zero buildings are achievable via photovoltaic windows when combined with careful geometric considerations.

14 SOLAR ENERGY↗
NASA NTRS1976

Solar hot water systems application to the solar building test facility and the Tech House

Projects which relate to the current national thrust toward demonstrating applied solar energy are discussed. The first project has as its primary objective the application of a system comprised of a flat plate collector field, an absorption air conditioning system, and a hot water heating system to satisfy most of the annual cooling and heating requirements of a large commercial office building. The other project addresses the application of solar collector technology to the heating and hot water requirements of a domestic residence. In this case, however, the solar system represents only one of several important technology items, the primary objective for the project being the application of space technology to the American home.

Goble, R. L.↗
DOE OSTI2023

Multidisciplinary concurrent optimization framework for multi-phase building design process

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. Here this paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1980

Solar heating and hot water system installed at Saint Louis, Missouri

The solar heating and hot water system installed at the William Tao & Associates, Inc., office building in St. Louis, Missouri is described, including maintenance and construction problems, final drawings, system requirements, and manufacturer's component data. The solar system was designed to provide 50 percent of the hot water requirements and 45 percent of the space heating needs for a 900 sq ft office space and drafting room. The solar facility has 252 sq ft of glass tube concentrator collectors and a 1000 gallon steel storage tank buried below a concrete slab floor. Freeze protection is provided by a propylene glycol/water mixture in the collector loop. The collectors are roof mounted on a variable tilt array which is adjusted seasonally and is connected to the solar thermal storage tank by a tube-in-shell heat exchanger. Incoming city water is preheated through the solar energy thermal storage tank.

Source record↗
DOE OSTI2021

Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven stochastic decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) consider complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematically integrate zonal occupancy patterns to better identify short-term (and time-varying) opportunities for grid service participation. We evaluate the performance of the proposed framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone commercial office building for a range of design choices. With the help of a prototype system that integrates an interactive \textit{Data Analytics and Visualization} frontend we demonstrate a way for the building operators to monitor and change in real-time the available flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.

Jain, Milan↗
DOE OSTI2022

AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience

Rapid deployment of residential photovoltaic (PV) systems helps decarbonize our electricity supplies, but under certain circumstances, high-penetration PV may pose challenges to the electrical distribution grid. In a project funded by the U.S. Department of Energy's Solar Energy Technologies Office and Building Technologies Office, the National Renewable Energy Laboratory and its partners studied how flexible building loads and battery storage, when coordinated at home-level and community-level scales, can be used to address those challenges and enhance grid resilience. In this webinar, we will discuss the methodology, simulation and field pilot results, insights from partners, and lessons learned from the project.

artificial intelligence↗
DOE OSTI2023

Implementation and test of an automated control hunting fault correction algorithm in a fault detection and diagnostics tool

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Application of the DNAS framework expansion to occupant population synthesis

Research in occupant behaviour is now using a more elaborate framework of building occupant interaction. Researchers often face challenges in collecting data, particularly for the data to meet the minimum number of required data points and the data interoperability requirements. Researchers address the first issue with the synthetic population and the latter with data ontologies. While synthetic population is commonly used to address the first issue, data ontology development is used to address the latter. The two solutions are complementary to each other. One of the known ontologies in building occupant behaviour research is the Drivers-Needs-Actions-Systems (DNAS) ontology, which has been used by building modelers to describe energy-related occupant behaviour. This paper describes the ontology-based synthetic population generation that can be used in the agent-based modeling (ABM) applications. This paper considers multiple data sources, including ASHRAE Thermal Comfort DB II and IEA Annex 66 data sets. A case study of an office building is used to present the workflow of DNAS framework expansion, synthetic population generation, and agent-based modeling.

Chandra Putra, Handi↗
NASA NTRSDate not supplied

SCaN: Space Communication and Navigation - 5G in Space

NASA’s Space Communications and Navigation (SCaN) program office builds, operates, and maintains NASA’s two primary communications networks – the Near Space Network and the Deep Space Network. These networks allow spacecraft near and far to send critical tracking, telemetry, command, and science data to Earth. SCaN also investigates new communications and navigation technologies so that these networks can support advanced missions. SCaN’s Commercialization, Innovation, and Synergies (CIS) office is dedicated to creating robust partnerships with industry, academia, and other government agencies to advance communications and navigation capabilities.

5G↗
DOE OSTI2023

Dilated causal convolutional neural networks for forecasting zone airflow to estimate short-term energy consumption

Here this paper investigates the use of dilated causal convolutional neural networks for fine- grained temporal forecasting of building zone states. Specifically, we build and evaluate models using a small set of exogenous features (e.g., external temperature) to autoregressively predict zone airflow setpoints every minute for a 24-hour prediction window. We carefully explore the trade-off between generality and specificity in these models, training and evaluating them based on zone, zone type, month, season, and combinations thereof. When evaluated for a commercial office building in Eastern Washington with 16 zones served by variable air volume air handling units, we find that the highest performance comes from a zone-specific, season-agnostic approach; with it, we obtain an R 2 of 0.704 (averaged over zones) and an average normalized root mean square error (nRMSE) of 0.111. In contrast, the most general model (trained across all zones and seasons) yields an R 2 of only 0.416 and a nRMSE of 0.168, while a baseline zone-specific reduced order model obtains 0.443 R 2 and 0.159 nRMSE. We also report on factors affecting airflow forecasting performance, on the ability of models trained on a specific zone to generalize to other zones, and on the capability of those models trained on a specific month to generalize to other months.

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 OSTI2023

Sensor Incipient Fault Impacts on Building Energy Performance: A Case Study on a Multi-Zone Commercial Building

Existing studies show sensor faults/error could double building energy consumption and carbon emissions compared with the baseline. Those studies assume that the sensor error is fixed or constant. However, sensor faults are incipient in real conditions and there were extremely limited studies investigating the incipient sensor fault impacts systematically. This study filled in this research gap by studying time-developing sensor fault impacts to rule-based controls on a 10-zone office building. The control sequences for variable air volume boxes (VAV) with an air handling unit (AHU) system were selected based on ASHRAE Guideline 36-2018: High-Performance Sequences of Operation for HVAC Systems. Large-scale simulations on cloud were conducted (3600 cases) through stochastic approach. Results show (1) The site energy differences could go –3.3% lower or 18.1% higher, compared with baseline. (2) The heating energy differences could go –66.5% lower or 314.4% higher, compared with baseline. (3) The cooling energy differences could go –11.5% lower or 65.0% higher, compared with baseline. (4) The fan energy differences could go 0.15% lower or 6.9% higher, compared with baseline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗