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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Kinetics of low temperature plasma assisted NH 3 /H 2 oxidation in a nanosecond-pulsed discharge

Ammonia (NH 3 ) has been widely recognized as one of the carbon-neutral fuels. However, ammonia combustion suffers low reactivity and high N 2 O/NO x emissions. Here, to overcome these issues, this work reports plasma assisted NH 3 /H 2 oxidation and unveils the kinetics of fuel oxidation and N 2 O/NO x formation by combining time-resolved laser diagnostics with plasma modeling. Firstly, we found that the NH 3 consumption is promoted with a H 2 blending ratio of 0.3, due to enhancements of H and OH formation by plasma assisted H 2 dissociation. Secondly, at a high reduced electric field, when the H 2 blending ratio increases, the NH 3 oxidation is promoted due to both the HO 2 formation and strong NO kinetic enhancement via NO-HO 2 and NO 2 -H pathways. In the meantime, it is shown that the NO mole fraction also increases with H 2 blending ratio, because the NO formation is enhanced via N( 2 D)-O 2 pathways, and the DeNO x chemistry is weakened with less NH 2 production. By contrast, at a lower reduced electric field, when the H 2 blending ratio increases, the decreased N( 2 D) formation does not produce enough NO to replenish the NO formation drop caused by lower NH 3 concentration. Thirdly, the reduced electric field non-monotonically affects fuel consumption and N 2 O/NO x formation by manipulating electron energy deposition pathways. The NH 3 consumption is maximized with an optimal reduced electric field where N 2 * excitation and O 2 dissociation are most efficient. When the reduced electric field deviates from its optimum, the NH 3 consumption decreases due to the discharge energy deposition to either vibrational excitation or dissociation of N 2 . The N 2 O/NO x emissions governed by the NH 3 oxidation follow the above NH 3 consumption trend.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Reactive oxygen species produced by NADPH oxidase and mitochondrial dysfunction in lung after an acute exposure to Residual Oil Fly Ashes

Reactive O{sub 2} species production triggered by particulate matter (PM) exposure is able to initiate oxidative damage mechanisms, which are postulated as responsible for increased morbidity along with the aggravation of respiratory diseases. The aim of this work was to quantitatively analyse the major sources of reactive O{sub 2} species involved in lung O{sub 2} metabolism after an acute exposure to Residual Oil Fly Ashes (ROFAs). Mice were intranasally instilled with a ROFA suspension (1.0 mg/kg body weight), and lung samples were analysed 1 h after instillation. Tissue O{sub 2} consumption and NADPH oxidase (Nox) activity were evaluated in tissue homogenates. Mitochondrial respiration, respiratory chain complexes activity, H{sub 2}O{sub 2} and ATP production rates, mitochondrial membrane potential and oxidative damage markers were assessed in isolated mitochondria. ROFA exposure was found to be associated with 61% increased tissue O{sub 2} consumption, a 30% increase in Nox activity, a 33% increased state 3 mitochondrial O{sub 2} consumption and a mitochondrial complex II activity increased by 25%. During mitochondrial active respiration, mitochondrial depolarization and a 53% decreased ATP production rate were observed. Neither changes in H{sub 2}O{sub 2} production rate, nor oxidative damage in isolated mitochondria were observed after the instillation. After an acute ROFA exposure, increased tissue O{sub 2} consumption may account for an augmented Nox activity, causing an increased O{sub 2}{sup ·−} production. The mitochondrial function modifications found may prevent oxidative damage within the organelle. These findings provide new insights to the understanding of the mechanisms involving reactive O{sub 2} species production in the lung triggered by ROFA exposure. - Highlights: • Exposure to ROFA alters the oxidative metabolism in mice lung. • The augmented Nox activity contributes to the high tissue O{sub 2} consumption. • Exposure to ROFA produces alterations in mitochondrial function. • ΔΨ{sub m} decrease in state 3 may be responsible for the decreased ATP production. • Mild uncoupling prevents mitochondrial oxidative damage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Thermal and mechanical stabilization process of the organic fraction of the municipal solid waste

Graphical abstract: Display Omitted - Highlights: • A domestic scale prototype for the pre-treatment of OFMSW has been tested. • Two grinding techniques are compared and thermopress is used for the drying stage. • Increasing temperature up to 170 °C reduces energy consumption of the drying stage. • In the range 5–10 bar a reduction of 97% of the initial volume is obtained. • In most cases energy recovery from the dried waste matches energy consumption. - Abstract: In the present study a thermo-mechanical treatment for the disposal of the Organic Fraction of Municipal Solid Waste (OFMSW) at apartment or condominium scale is proposed. The process presents several advantages allowing to perform a significant volume and moisture reduction of the produced waste at domestic scale thus producing a material with an increased storability and improved characteristics (e.g. calorific value) that make it available for further alternative uses. The assessment of the applicability of the proposed waste pretreatment in a new scheme of waste management system requires several research steps involving different competences and application scales. In this context, a preliminary study is needed targeting to the evaluation and minimization of the energy consumption associated to the process. To this aim, in the present paper, two configurations of a domestic appliance prototype have been presented and the effect of some operating variables has been investigated in order to select the proper configuration and the best set of operating conditions capable to minimize the duration and the energy consumption of the process. The performances of the prototype have been also tested on three model mixtures representing a possible daily domestic waste and compared with an existing commercially available appliance. The results obtained show that a daily application of the process is feasible given the short treatment time required and the energy consumption comparable to the one of the common domestic appliances. Finally, the evaluation of the energy recovered in the final product per unit weight of raw material shows that in most cases it is comparable to the energy required from the treatment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Evaluation of Analytical and Numerical Techniques for Defining the Radius of Influence for an Open-Loop Ground Source Heat Pump System

In an open-loop groundwater heat pump (GHP) system, groundwater is extracted, run through a heat exchanger, and injected back into the ground, resulting in no mass balance changes to the flow system. Although the groundwater use is non-consumptive, the withdrawal and injection of groundwater may cause negative hydraulic and thermal impacts to the flow system. Because GHP is a relatively new technology and regulatory guidelines for determining environmental impacts for GHPs may not exist, consumptive use metrics may need to be used for permit applications. For consumptive use permits, a radius of influence is often used, which is defined as the radius beyond which hydraulic impacts to the system are considered negligible. In this paper, the hydraulic radius of influence concept was examined using analytical and numerical methods for a non-consumptive GHP system in southeastern Washington State. At this location, the primary hydraulic concerns were impacts to nearby contaminant plumes and a water supply well field. The results of this study showed that the analytical techniques with idealized radial flow were generally unsuited because they over predicted the influence of the well system. The numerical techniques yielded more reasonable results because they could account for aquifer heterogeneities and flow boundaries. In particular, the use of a capture zone analysis was identified as the best method for determining potential changes in current contaminant plume trajectories. The capture zone analysis is a more quantitative and reliable tool for determining the radius of influence with a greater accuracy and better insight for a non-consumptive GHP assessment.

GHPs↗
DOE OSTI2026

Measuring impacts of California agri-environmental programs using field-scale satellite data

In the past decade, California has invested over $\$$200 million in direct grants to growers to support the adoption of agricultural practices that save water and/or improve soil health while also reducing greenhouse gas emissions. Ex-post evaluation of agri-environmental outcomes of these grant programs, however, is limited. We use satellite data to monitor changes in field-level consumptive water use and greenness (i.e. normalized difference vegetation index), a proxy for agricultural productivity, for the most frequently funded crop-types (almonds, grapes, and walnuts) in two California Department of Food and Agriculture programs. Nearly 600 fields receiving funding during the 2014–2022 period were analyzed using two causal inference methods. Fields that received grants to both upgrade irrigation systems and install irrigation water management sensors showed reduced consumptive water use and greenness by an average of 3.5% and 4.2%, respectively (significant at the 10% level). In contrast, we find that the adoption of only irrigation water management sensors, which are designed to inform irrigation scheduling and management, resulted in an average increase of 4.1% and 4.8% in consumptive water use and greenness respectively (significant at the 5% level). We find negligible effects for either consumptive water use or greenness when both pump efficiency upgrades and sensors were implemented. We further find that grants for compost addition and cover cropping led to small greenness increases of 1.7% and 2.8% respectively (significant at the 10% level) and had insignificant effects on consumptive water use. Our analysis of five agri-environmental program interventions reveals that several practice outcomes may be at odds with stated program goals of reducing water use while maintaining or improving agricultural productivity.

agriculture↗
DOE OSTI2026

Validating and Comparing Energy Estimation Methods at Water Resource Recovery Facilities

Water resource recovery facilities play a crucial role in the water-energy nexus, consuming a substantial amount of energy in the United States. Growing treatment volumes and more stringent water quality standards are expected to increase the amount of energy needed to treat wastewater, but accurately estimating energy consumption and potential remains challenging due to variability in scale, treatment methods, and effluent treatment standards. In this study, we used publicly available data to evaluate the accuracy of methods for estimating energy consumption and generation, then quantified uncertainty based on key factors like flow rate, treatment level, and geographic location. To validate methods, we estimated energy consumption and generation at the facility-level, then compared estimates to self-reported data from utilities in major U.S. cities. We found that process models of treatment trains under best practice configurations were accurate relative to other methods for estimating electricity use, total energy use, and electricity generation from biogas utilization, and less complex methods based on effluent treatment level and prime movers also performed well for estimating electricity consumption and generation, respectively. Applying the evaluated methods to a national inventory of treatment facilities, we estimate that annual energy consumption ranged from 56.3 x 10^3 to 82.5 x 10^3 TJ in 2012 and 83.6 x 10^3 to 127 x 10^3 TJ in 2042. Our results indicate that not all estimation methods are suited for every use case, so we recommend that researchers and practitioners select an estimation method based on data availability and desired computational intensity.

Hodson, Abigayle↗
DOE OSTI2023

Analysis of Uncertainty Impacts on Emissions and Fuel Economy Evaluation for Chassis Dynamometer Testing

This study illustrates a methodology for quantifying the uncertainties encountered in the measurement of tailpipe emissions and in the fuel consumption measurements for light-duty conventional vehicles tested on a four-wheel drive chassis dynamometer. The study leverages high-fidelity experimental data collected over three standard drive cycles, UDDS, HWY and US06, intended to simulate a wide range of operating conditions. Here, a method is developed to estimate the measurement uncertainties in fuel consumption for a test cycle, which occur due to the accumulation of measurement uncertainties propagated through the system. The uncertainty determination model uses statistical analysis and standard propagation techniques to evaluate and combine the uncertainties introduced from various sources (including the vehicle, chassis dynamometer, driver, and instrumentation). The analysis also examines three different experimental methods for determining the fuel consumption: 1) carbon mass balance, 2) volumetric fuel scale and 3) gravimetric fuel scale, and takes into consideration the properties of the instrumentation used. The results show that the most significant influence on the determination of the emissions comes from the concentration measurement, and similarly the biggest impact on the total fuel consumption uncertainty comes from the uncertainty in the determination of the carbon dioxide mass, due to the large presence of this pollutant in the overall emissions. It was found that the fuel consumption uncertainties are in the range of ±1-2% for all three methods analyzed, with the lowest values being obtained for measurements performed using the gravimetric method for all three drive cycles considered.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Data Informed Vehicle Trip Energy Estimation [SWR-17-30]

There is a need to accurately estimate trip energy consumption for vehicle trips where high resolution GPS data is not available, or where the trips are simply proposed and have not been driven. This need is most apparent in green routing algorithms, it is critical to accurately estimate the energy consumption for a route that has not been driven. The Data Informed Energy Estimation Software addresses this need by identifying the driving and roadway attributes most impactful on fuel consumption, and using large amounts of high resolution GPS data from real world driving to determine the average fuel consumption for all combinations of attributes. These attributes can include traffic conditions, vehicle speed, and road gradient among others. This aggregation technique results in a lookup table that provides energy consumption values for combinations of road and driving techniques. This lookup table is an extremely lightweight and versatile result that can be applied in a number of applications.

Holden, Jacob↗
DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

Engineering↗
DOE OSTI2020

Non-routine adjustments – towards standardizing M&V approach for quantifying the effects of static factors

The success of an energy management program relies on ensuring that the energy savings can be verified with an adequate level of certainty. Energy savings cannot be directly measured and hence have to be deduced by comparing the pre-retrofit energy consumption with post-retrofit energy consumption data adjusted to account for differences in conditions. These adjustments are needed, since conditions that influence energy consumption may not stay the same between pre- and postinstallation phase of the project. These adjustments can often be routine when accounting for factors like production volume and weather that are expected to change and are included in the energy consumption adjustment model. Existing measurement and verification (M&V) resources and guidance mostly concentrate on developing models for routine adjustment of one or more factors that normally change. On the other hand, there are factors (static) like product mix, operating hours, gross square area, etc. that are assumed to stay constant during normal conditions. However, in order to adapt to dynamic market conditions, the industries are forced to react thereby leading to changes to these static factors. Identifying these static factors that warrant adjustment, called non-routine event, along with quantifying their effect on energy consumption can be complex and lack of proper guidance can exacerbate this issue. This paper reviews some of the current work in terms of how non-routine events are defined, characterized, detected and quantified based on a review of existing M&V guidelines, protocols and other relevant literature. This work also reviewed some of the existing non-routine events and adjustment practices to understand how these different aspects are addressed along with a discussion on some of the key challenges and gaps in the current guidance.

Earni, Shankar↗
DOE OSTI2018

Supply Chain Sustainability Analysis of Renewable Hydrocarbon Fuels via Indirect Liquefaction, Ex Situ Catalytic Fast Pyrolysis, Hydrothermal Liquefaction, Combined Algal Processing, and Biochemical Conversion: Update of the 2018 State-of-Technology Cases and Design Cases

This technical report describes the SCSAs for the production of renewable hydrocarbon transportation fuels via a range of conversion technologies: (1) renewable high octane gasoline (HOG) via indirect liquefaction (IDL) of woody lignocellulosic biomass (note that the IDL pathway in this SCSA represents the syngas conversion design in the 2018 SOT and 2022 design cases [Tan et al., 2018]); (2) renewable gasoline (RG) and diesel (RD) blendstocks via ex situ catalytic fast pyrolysis of woody lignocellulosic biomass; (3) RD via hydrothermal liquefaction (HTL) of wet sludge from a wastewater treatment plant; (4) renewable hydrocarbon fuels via biochemical conversion of herbaceous lignocellulosic biomass; (5) renewable diesel via HTL of a blend of algae and woody biomass; and (6) renewable diesel via combined algae processing (CAP). This technical report focuses on the environmental performance of these six biofuel production pathways in their 2018 SOT cases, as well as in their design cases (future target projections). The results of these renewable hydrocarbon fuel pathways in these SCSA analyses update those for the respective 2015 and 2016 SOT cases (Edward Frank et al. 2016; Hao Cai et al. 2016, 2017; Cai et al. 2018) in the case of IDL, algae CAP, and biochemical conversion pathways. They also provide an opportunity to examine the impact of technology improvements in both biomass feedstock production and biofuel production that have been achieved in 2018 SOTs on the sustainability performance of these renewable transportation fuels, and they reflect updates to Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET®) model, which was released in October 2018 (Wang et al. 2018). These GREET updates include production of natural gas, electricity, and petroleum-based fuels that can influence biofuels’ supply chain greenhouse gas (GHG) (CO2, CH4, and N2O) emissions, water consumption and air pollutant emissions. GHG emissions, water consumption, and 2 nitrogen oxides (NOx) emissions are the main sustainability metrics assessed in this analysis. In this analysis, we define water consumption as the amount of water withdrawn from a freshwater source that is not returned (or returnable) to a freshwater source at the same level of quality. Life-cycle fossil energy consumption and net energy balance, which is the life-cycle fossil energy consumption deducted from the renewable biofuel energy produced, are also assessed.

02 PETROLEUM↗
DOE OSTI2018

Supply Chain Sustainability Analysis of Renewable Hydrocarbon Fuels via Indirect Liquefaction, Ex Situ Catalytic Fast Pyrolysis, Hydrothermal Liquefaction, Combined Algal Processing, and Biochemical Conversion: Update of the 2018 State-of-Technology Cases and Design Cases

This technical report describes the SCSAs for the production of renewable hydrocarbon transportation fuels via a range of conversion technologies: (1) renewable high octane gasoline (HOG) via indirect liquefaction (IDL) of woody lignocellulosic biomass (note that the IDL pathway in this SCSA represents the syngas conversion design in the 2018 SOT and 2022 design cases [Tan et al., 2018]); (2) renewable gasoline (RG) and diesel (RD) blendstocks via ex situ catalytic fast pyrolysis of woody lignocellulosic biomass; (3) RD via hydrothermal liquefaction (HTL) of wet sludge from a wastewater treatment plant; (4) renewable hydrocarbon fuels via biochemical conversion of herbaceous lignocellulosic biomass; (5) renewable diesel via HTL of a blend of algae and woody biomass; and (6) renewable diesel via combined algae processing (CAP). This technical report focuses on the environmental performance of these six biofuel production pathways in their 2018 SOT cases, as well as in their design cases (future target projections). The results of these renewable hydrocarbon fuel pathways in these SCSA analyses update those for the respective 2015 and 2016 SOT cases (Edward Frank et al. 2016; Hao Cai et al. 2016, 2017; Cai et al. 2018) in the case of IDL, algae CAP, and biochemical conversion pathways. They also provide an opportunity to examine the impact of technology improvements in both biomass feedstock production and biofuel production that have been achieved in 2018 SOTs on the sustainability performance of these renewable transportation fuels, and they reflect updates to Argonne National Laboratory's Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET[R]) model, which was released in October 2018 (Wang et al. 2018). These GREET updates include production of natural gas, electricity, and petroleum-based fuels that can influence biofuels' supply chain greenhouse gas (GHG) (CO 2 , CH 4 , and N 2 O) emissions, water consumption and air pollutant emissions. GHG emissions, water consumption, and 2 nitrogen oxides (NO x ) emissions are the main sustainability metrics assessed in this analysis. In this analysis, we define water consumption as the amount of water withdrawn from a freshwater source that is not returned (or returnable) to a freshwater source at the same level of quality. Life-cycle fossil energy consumption and net energy balance, which is the life-cycle fossil energy consumption deducted from the renewable biofuel energy produced, are also assessed.

09 BIOMASS FUELS↗
DOE OSTI2021

Life Cycle Inventories for Palladium on Niobium Phosphate (Pd/NbOPO 4 ) and Zirconium Oxide (ZrO 2 ) Catalysts

We report the cradle-to-gate GHG emissions, fossil fuel consumption, and water consumption for Nb 2 O 5 , KNbO 3 , NbOPO 4 , Pd, Pd/NbOPO 4 , ZrSiO 4 , and ZrO 2 . Notably, the net GHG emissions impact of the Pd/NbOPO 4 catalyst is 8.5 kg CO 2 e/kg catalyst, which is comparable to other catalysts in GREET, while the net GHG emissions of the ZrO 2 catalyst is considerably lower at 1.8 kg CO 2 e/kg catalyst. We also identify the primary contributors to each catalyst’s cradle-to-gate environmental burden. For the Pd/NbOPO 4 catalyst, Pd metal is the main driver of GHG emissions and fossil fuel consumption, while Pd and NbOPO 4 contribute almost equally to water consumption. For the ZrO 2 catalyst, sodium hydroxide (NaOH) is the principal driver of GHG emissions and fossil fuel consumption, while ZrSiO 4 is the main consumer of water. The Pd/NbOPO 4 and ZrO 2 catalysts, as well as Nb 2 O 5 , KNbO 3 , NbOPO 4 , and ZrSiO 4 , are implemented in the GREET catalyst module, and Pd has been implemented in GREET2 (Kingsbury and Benavides 2021). The material and energy flows for these new materials will be useful to future LCAs, particularly those involving biofuel production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Supply Chain Sustainability Analysis of Renewable Hydrocarbon Fuels via Indirect Liquefaction, Ex Situ Catalytic Fast Pyrolysis, Hydrothermal Liquefaction, Combined Algal Processing, and Biochemical Conversion: Update of the 2020 State-of-Technology Cases

This technical report describes the SCSAs for the production of renewable hydrocarbon transportation fuels via a range of conversion technologies in the 2020 SOTs: (1) renewable high octane gasoline (HOG) via indirect liquefaction (IDL) of woody lignocellulosic biomass (note that the IDL pathway in this SCSA represents the syngas conversion design); (2) renewable gasoline (RG) and diesel (RD) blendstocks via ex situ catalytic fast pyrolysis of woody lignocellulosic biomass; (3) RD via hydrothermal liquefaction (HTL) of wet sludge from a wastewater treatment plant; (4) renewable hydrocarbon fuels via biochemical conversion of herbaceous lignocellulosic biomass; (5) renewable diesel via HTL of a blend of algae and woody biomass; and (6) renewable diesel via combined algae processing (CAP). This technical report focuses on the environmental performance of these six biofuel production pathways in their 2020 SOT cases. The results of these renewable hydrocarbon fuel pathways in these SCSA analyses update those for the respective 2019 SOT cases. They also provide an opportunity to examine the impact of technology improvements in both biomass feedstock production and biofuel production that have been achieved in 2020 SOTs on the sustainability performance of these renewable transportation fuels. The SCSA results also reflect updates to Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET ® ) model, which was released in October 2020. These GREET updates include the production of natural gas, electricity, and petroleum-based fuels that can influence biofuels’ supply chain greenhouse gas (GHG) (CO 2 , CH 4 , and N 2 O) emissions, water consumption, and air pollutant emissions. GHG emissions, water consumption, and nitrogen oxides (NO x ) emissions are the main sustainability metrics assessed in this analysis. In this analysis, we define water consumption as the amount of water withdrawn from a freshwater source that is not returned (or returnable) to a freshwater source at the same level of quality. Life-cycle fossil energy consumption and net energy balance, which is the life-cycle fossil energy consumption deducted from the renewable biofuel energy produced, are also assessed.

09 BIOMASS FUELS↗
DOE OSTI2018

BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting

The BuildingsBench datasets consist of: - Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. - 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: 1. ElectricityLoadDiagrams20112014 2. Building Data Genome Project-2 3. Individual household electric power consumption (Sceaux) 4. Borealis 5. SMART 6. IDEAL 7. Low Carbon London A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

Array↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2017

Investigation of Transmission Warming Technologies at Various Ambient Conditions

This work details two approaches for evaluating transmission warming technology: experimental dynamometer testing and development of a simplified transmission efficiency model to quantify effects under varied real world ambient and driving conditions. Two vehicles were used for this investigation: a 2013 Ford Taurus and a 2011 Ford Fusion. The Taurus included a production transmission warming system and was tested over hot and cold ambient temperatures with the transmission warming system enabled and disabled. A robot driver was used to minimize driver variability and increase repeatability. Additionally the Fusion was tested cold and with the transmission pre-heated prior to completing the test cycles. These data were used to develop a simplified thermally responsive transmission model to estimate effects of transmission warming in real world conditions. For the Taurus, the fuel consumption variability within one standard deviation was shown to be under 0.5% for eight repeat Urban Dynamometer Driving Cycles (UDDS). These results were valid with the transmission warming system active or passive. Using the transmission warming system under 22 degrees C ambient temperature, fuel consumption reduction was shown to be 1.4%. For the Fusion, pre-warming the transmission reduced fuel consumption 2.5% for an urban drive cycle at -7 degrees C ambient temperature, with 1.5% of the 2.5% gain associated with the transmission, while consumption for the US06 test was shown to be reduced by 7% with 5.5% of the 7% gain associated with the transmission. It was found that engine warming due to conduction between the pre-heated transmission and the engine resulted in the remainder of the benefit. For +22 degrees C ambient tests, the pre-heated transmission was shown to reduce fuel consumption approximately 1% on an urban cycle, while no benefit was seen for the US06 cycle. The simplified modeling results showed gains in efficiency ranging from 0-1.5% depending on the ambient temperature and drive cycle.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Engine-in-the-Loop Study of a Hierarchical Predictive Online Controller for Connected and Automated Heavy-Duty Vehicles

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing. This work is experimentally experimentally validated via a sequence of EIL studies intended for evaluating the computational costs and fuel savings associated with these algorithms. These EIL studies involve the closed-loop validation of the proposed control strategies, both individually and combined. These studies show that this hierarchy of algorithms is capable of running online, with the round-trip communication delay inherent in EIL simulation being one of the key factors affecting the EIL results. Moreover, the EIL studies are encouraging, both in terms of the successful hierarchical integration of the underlying algorithms and also in the resulting fuel savings seen in the EIL tests. In particular, the EIL results suggest that an aggressive overall goal of reducing vehicle fuel consumption by 15-20% or more is potentially achievable, especially in urban/suburban scenarios.

33 ADVANCED PROPULSION SYSTEMS↗