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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Trade-offs between Biofuels Energy Production, Land Use and Water Use in Florida

Objective of the presentation is to document land use and water use implications of biomass production to demonstrate the overall resources implications associated with bioethanol production for Florida’s transportation sector needs. Rationale for using biofuels (BF) is explained, so are advantages & challenges of BF production and use. Land use changes (LUC) in Florida are presented and consequences outlined. It is documented that Florida’s agricultural land is a very limited resource, with only 0.43 ac/person comparing to the global average of 1.71 ac/person. The direct relation of increased biofuels production causing increased water use is explained. Favorable climate, water resources, advanced research, traditional leading agricultural role, minor oil reserves, no refineries and increasing energy demands are the main reasons why Florida considers pursuing BF production in large scale. Eight various bioethanol crops produced in Florida were considered in this study (Miscanthus, Switchgrass, Sweet Sorghum, Corn, Elephantgrass, Sugarcane, Energycane, Eucalyptus). Biomass yield and bioethanol yield of these crops are documented. Bioethanol needs of Florida are estimated and related land requirements for the needed bioethanol production calculated. Projections for various bioethanol blends (E15 to E85) are then presented. Finally, water demand for biofuels production is quantified. It is concluded that land use requirement for production of all ethanol in E85 fuel blend in Florida is roughly the same as the total available ag land in Florida for the best yielding biofuels crops (energycane, eucalyptus). Water demand for production of all ethanol needed for E100 would increase current overall water consumption in Florida between 65% and 100% for the most common biofuels crops. Vehicular energy is only 33% of Floridians energy consumption, so even all Florida’s agricultural land was given up for biofuels, it would still produce only 33% of Florida’s total energy needs. Still, bioethanol (primarily cellulosic) produced in Florida has a potential to meet a significant portion of the State’s transportation needs. Assuming no change in food production and consumption habits in Florida, the likely result of biofuels sector expansion would be the conversion of natural lands or low-intensity agricultural lands into high-intensity biomass production and the associated increased water consumption and water quality implications.

09 BIOMASS FUELS↗
DOE OSTI2020

Assessing and Reducing Plug and Process Loads in Office Buildings

Plug and process loads (PPLs) account for 33% of U.S. commercial building electricity consumption (McKenney et al. 2010). (See Figure 1.) Minimizing these loads is a significant challenge in the design and operation of an energy-efficient building. Lobato et al. (2011) and Lobato et al. (2012) define PPLs as energy loads that are not related to general lighting, heating, ventilation, cooling, and water heating, and that typically do not provide comfort to the occupants. The percentage of total building energy use from PPLs is increasing. According to the U.S. Department of Energy (DOE), by 2030, commercial building energy consumption is expected to increase by 24%; PPL energy consumption is anticipated to increase by 49% in the same time frame (DOE 2010). These trends illustrate the importance of PPL energy reduction to achieve an overall goal of reducing whole-building energy consumption.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Assessing and Reducing Plug and Process Loads in Retail Buildings

Plug and process loads (PPLs) account for 33% of U.S. commercial building electricity consumption (McKenney et al. 2010). (See Figure 1.) Minimizing these loads is a significant challenge in the design and operation of an energy-efficient building. Lobato et al. (2011) and Lobato et al. (2012) define PPLs as energy loads that are not related to general lighting, heating, ventilation, cooling, and water heating, and that typically do not provide comfort to the occupants. The percentage of total building energy use from PPLs is increasing. According to the U.S. Department of Energy (DOE), by 2030, commercial building energy consumption is expected to increase by 24%; PPL energy consumption is anticipated to increase by 49% in the same time frame (DOE 2010). These trends illustrate the importance of PPL energy reduction to achieve an overall goal of reducing whole-building energy consumption.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2015

The effects of Suspended Sediment on Japanese Medaka (Oryzias latipes) and Mosquitofish (Gambusia affinis) Metabolism

Sedimentation is recognized as a significant environmental stressor in aquatic ecosystems and high amounts of suspended sediments (SS) in streams are known to negatively affect aquatic organisms. In particular, it has been hypothesized that many fish species may exhibit increased respiration rates when exposed to elevated SS. To evaluate this hypothesis, we evaluated the acute response (3 hour exposure) of two small, freshwater fish species exposed to high suspended sediment loads using experimental respirometry chambers which measure oxygen consumption as a proxy of metabolism. Our results indicate that Japanese Medaka (Oryzias latipes) did not exhibit a significantly greater oxygen consumption, as compared to control fish, when exposed to chamber sediment of 0.17 g/L (p-value of 0.41), though oxygen consumption was higher for sediment treatments. However, mosquitofish (Gambusia affinis) exposed to 0.17 g/L did exhibit noticeably greater oxygen consumption compared to fish in no-sediment control test, but not at statistically significant levels (p-value of 0.07). In this work, further study is needed with increased sample sizes and across SS levels to determine the threshold of increased metabolic rate among freshwater species exposed to SS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Customer outcomes in Pay-As-You-Save programs

We review the energy and financial outcomes of households participating in several programs based on successive versions of the Pay As You Save¯ (PAYS¯) system. PAYS¯ programs offer non-debt financing for energy efficiency (and sometimes other technologies) in residential buildings through a tariff attached to the home’s utility meter, designed to be offset by project savings. We find that the five programs we study generally serve customers living in zip codes with levels of income and education below the national average and unemployment rates above the national average, demonstrating their potential to improve equity in energy efficiency adoption. Using weather-normalized analysis of energy consumption data, we show that most customers of Midwest Energy’s program reduce annual electricity and gas consumption, averaging 15% and 26% reductions respectively. Changes in energy consumption calculated using this method represent a combination of project effects and changes in occupant behavior. These results are similar to existing analyses of PAYS¯ programs in North Carolina, Arkansas, and Tennessee. About half of participating Midwest households generate sufficient energy cost savings to cover their monthly tariff. Various factors, including changes in occupant behavior, program error, causes independent of the customer or program, or some combination thereof may explain lower-than-expected cost reductions in some projects. Given the inherent variability in annual household electricity consumption, we feel these programs are enabling energy efficiency improvements and their attendant co-benefits, including occupant health and comfort and reduced carbon emissions, while reasonably balancing energy savings and tariff costs. Pairing PAYS¯ with additional financial assistance, as well as promoting cost-effective measures such as air and duct sealing, could further broaden program participation by enabling additional projects to meet PAYS¯ program eligibility rules.

Deason, Jeff↗
DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Empirical Validation of UBEM: An Assessment of Bias in Urban Building Energy Modeling for Chicago

Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales.As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. The accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9%, and 1.1% after removing outliers. Contribution of statistically significant factors including building type, land use, age, and size to variance in UBEM bias is quantified.

Garg, Ankur↗
DOE OSTI2016

A distributed decision framework for building clusters with different heterogeneity settings

In the past few decades, extensive research has been conducted to develop operation and control strategy for smart buildings with the purpose of reducing energy consumption. Besides studying on single building, it is envisioned that the next generation buildings can freely connect with one another to share energy and exchange information in the context of smart grid. It was demonstrated that a network of connected buildings (aka building clusters) can significantly reduce primary energy consumption, improve environmental sustainability and building s resilience capability. However, an analytic tool to determine which type of buildings should form a cluster and what is the impact of building clusters heterogeneity based on energy profile to the energy performance of building clusters is missing. To bridge these research gaps, we propose a self-organizing map clustering algorithm to divide multiple buildings to different clusters based on their energy profiles, and a homogeneity index to evaluate the heterogeneity of different building clusters configurations. In addition, a bi-level distributed decision model is developed to study the energy sharing in the building clusters. To demonstrate the effectiveness of the proposed clustering algorithm and decision model, we employ a dataset including monthly energy consumption data for 30 buildings where the data is collected every 15 min. It is demonstrated that the proposed decision model can achieve at least 13% cost savings for building clusters. Furthermore, the results show that the heterogeneity of energy profile is an important factor to select battery and renewable energy source for building clusters, and the shared battery and renewable energy are preferred for more heterogeneous building clusters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Solar Energy Integration in Buildings

Energy consumption in buildings has been steadily increasing and contributing up to 40% of the total energy use in developed countries [1]. In developing countries, the share of building energy consumption is smaller, but given population growth, urbanization, and rising demands for building services and comfort, the sharp rise of building energy use is probably inevitable. Thus, reducing building energy consumption plays a very important role in controlling global energy demand and mitigating climate change, so as to develop a sustainable environment. Solar energy, as the most important source of renewable energy, features the characteristics of clean, renewable, inexhaustible, and widely distributed energy, relative to other kinds of energy sources. Solar energy systems can now generate electricity at a cost equal to or lower than local grid-supplied electricity [2]. More importantly, solar energy can provide almost all forms of energy needed by buildings, through active or passive methods.

buildings↗
DOE OSTI2018

Comparative study of air-conditioning energy use of four office buildings in China and USA

Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. Here, this study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead to AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. In conclusion, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

This study evaluates the energy performance of ASHRAE Guideline 36–compliant control (ASHRAE 36 control) and reinforcement learning (RL)–based control through experimental field tests and a simulation study. Three field tests were conducted at Oak Ridge National Laboratory’s commercial building test facility in Oak Ridge, Tennessee: a baseline with a baseline conventional control, a test with ASHRAE 36 control, and a test with RL-based control. The selected ASHRAE 36 controls were trim and respond control, as well as variable air volume (VAV) box control. We compared the measured supply air temperature of the rooftop unit, VAV box supply air temperature, and VAV box supply airflow rate across the three test cases. The field data indicated that ASHRAE 36 controls operated as specified by ASHRAE Guideline 36. Based on these data, ASHRAE 36 control achieved a 45 % reduction in hourly averaged HVAC energy consumption compared with the baseline, and RL-based control achieved a 66 % reduction. These potential annual energy savings were confirmed using a calibrated whole-building energy model. Compared with the baseline, ASHRAE 36 control reduced HVAC energy consumption by 42 %, and RL-based control achieved a 54 % reduction. Furthermore, RL-based control reduced total HVAC energy consumption by 21 % more than ASHRAE 36 control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

The dual-credit policy: Quantifying the policy impact on plug-in electric vehicle sales and industry profits in China

We report that the Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation (dual-credit policy) was enacted by the Chinese government in 2017 to stimulate the fuel-efficient and electrification technologies in the China's passenger vehicle market. Here, this study summarizes the dual-credit policy and develops the New Energy and Oil Consumption Credits Model to quantify the impacts of this policy on consumer choices and industry profits, where internal subsidies as decision variables are used to represent industry responses to the policy. Scenarios in 2016–2020 are simulated and discussed. Key findings from the model results include: (1) the Corporate Average Fuel Consumption rules alone may stimulate more plug-in electric vehicle (PEV) sales than the dual-credit policy; however, (2) the dual-credit policy could stimulate more battery electric vehicles (BEVs) in market, compared to other policy scenarios; (3) the industry could “lose” approximately $2122/vehicle by 2020 under the dual-credit policy; (4) battery electric sedans with a range greater than 250 km and plug-in hybrid SUVs could be popular under the dual-credit policy; (5) credit allocations for BEVs in the dual-credit policy can influence the PEV production; and (6) reduction of the fuel-efficient technology costs helps to minimize profit losses impacted by the policy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

What goes on behind closed doors? How college dormitory residents change to save energy during a competition-based energy reduction intervention

Here, the aim of our study was to evaluate the impact of a competition-based intervention combining high resolution electricity feedback, incentives, information, and prompts on college dormitory residents’ energy consumption and participation in demand response (DR) events. We also investigated changes in individual-level pro-environmental behaviors and examined psycho-social correlates of behavior change. Residents of 39 suites in a freshman residence hall competed against one another to reduce energy consumption and win prizes as part of a 3-week competition. Feedback was provided in near real time at the suite-level via an interactive touch-screen kiosk. Participants also completed baseline and followup surveys. Findings have indicated that electricity use among all suites was approximately 6.4% lower during the competition period compared to baseline, a significant reduction. Additionally, participants reported engaging in various pro-environmental behaviors significantly more frequently during the competition relative to baseline. Changes in pro-environmental behavior were associated with changes in level of group identification and perceived social norms. In three weeks, dormitory residents saved 3, 158 kWh of electricity compared to baseline – the equivalent of more than 3, 470 pounds of carbon dioxide emissions. Our findings provide evidence that real-time feedback, combined with incentives, information, and prompts, can motivate on-campus residents to reduce energy consumption. We contribute to a limited body of evidence supporting the effectiveness of dorm energy competitions in motivating college students to save energy. In addition, we identified individual-level behavioral and psycho-social changes made during such an intervention. University residential life planners may also use the results of this research to inform student programming.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Harvesting from Ambient Energy: Designing Enabling Technologies for Sustainable Buildings

Electricity consumption in the commercial sector is forecasted to increase by 0.5% on an annual bases between 2017 to 2050. This is despite best design practices and adoption of more energy efficient resources for buildings. This increase is prevalent in miscellaneous electrical loads that studies have shown if their usage is tracked and conveyed to the occupant will result in up 34% consumption reduction. Diverse tools and research methodologies are being developed to track their location. This paper addresses one such method to identify the location of energy at the point of use using energy harvesting. The paper proposes a design process for magnetic flux harvesting around current carrying conductors of the electrical system in buildings for the purpose of powering sensors and/or for use as a triggering signal for energy consumption location in the built environment. Building on the electromagnetic induction theory, a relationship is developed between the size of the harvesting device and the total energy output. A prototype energy harvesting device based on the proposed data is built and tested through laboratory experiments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Demonstration of Intelligent HVAC Load Management With Deep Reinforcement Learning: Real-World Experience of Machine Learning in Demand Control

We report that buildings account for 40% of total primary energy consumption and 30% of all CO 2 emissions worldwide. A large portion of building energy consumption is due to heating, ventilation, and air-conditioning (HVAC) systems. In the summer, for example, more than 50% of a building’s electricity consumption is used for cooling. With proper energy management, buildings can provide load shifting, peak shaving, frequency regulation, and many other demand response services.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Study of Energy Saving Analysis for Different Industries

This study analyzes the energy consumption and saving performance in the industries in the U.S.A. All energy assessments implemented were for facilities whose annual energy consumptions were less than 9,000,000 kWh (small- and medium-sized industries) that belong to the manufacturing industries with Standard Industrial Classification (SIC) codes ranging from 2000 to 3999 in addition to SIC codes starting with 49. In this study, assessments are classified based on the SIC codes with recommendations analysis for each classification to get a better idea of what recommendations were suggested in each major industrial sector, knowing that 68 assessments were made, and their SIC ranged from 14 to 49. In addition, this study could be considered as a guide for energy engineers and other personnel involved in the energy assessment process. The information investigated can give a better prediction for composing better energy-demanding industries and minimizing energy consumption. More than 61 energy assessments were conducted for manufacturing facilities and analyzing the data gathered and processed. Through the research, the Fabricated Metal industry achieved the highest average kWh savings and cost savings within the industries studied in this study. According to the average gigajoule (GJ) savings, the fabricated metal industry ranked second within the studied industries. Conversely, Food and Kindred Products achieved the highest GJ energy savings within the studied industries. Overall, lighting, motors, compressors, and heating, ventilation, and air conditioning (HVAC) were the most contributing industries in a total of 547 recommendations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

RouteE API

This is the API endpoint for RouteE energy prediction, which can be used to get both single vehicle link or route energy estimates and transportation network-wide energy consumption estimates for a variety of vehicles. This enables external researchers and transportation engineers to access and utilize NLR's growing library of pre-trained vehicle models for prediction of transportation energy consumption. This API provides three endpoints: - /route: Energy estimation of a vehicle over a planning link or sequence of links (route). - /network: Network-wide estimation of energy consumption for all vehicle traffic in the desired area. - /compass: Energy-optimal “eco-routing” between input origin and destination coordinates (Currently in beta for Denver metro area only).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗