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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

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.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Overall chilled water system energy consumption modeling and optimization

The emergence of increasingly affordable variable-speed drive technology has changed the approach used to control chilled water systems equipped with these drives. The purpose of this research was to develop an integrated chilled water modeling technique that can determine the optimal system setpoints and estimate the energy saving potential of chiller system. The chiller system equipped with Variable Frequency Drives (VFDs) on cooling tower fans and condenser water pumps. To accomplish the objective, physical component models of the centrifugal chiller, cooling tower and condenser water pump were established with the goal of incorporating the system’s condenser water flow rate and cooling tower fan speeds as optimization variables. Furthermore, a cooling load prediction algorithm was developed using a multiple non-linear regression model to approximate the building’s cooling load subject to a range of environmental conditions. The inputs and outputs of the individual component models were linked to estimate how adjusting the cooling tower fan and condenser water pump speed would influence the system’s comprehensive performance. Here, the overall system model was then optimized using a generalized reduced gradient optimization algorithm to determine the potential energy savings through speed control with VFDs and to ascertain a control logic strategy for the building automation system to operate the heating and cooling system. A case-study was performed on a single chiller system at a museum and the model was calibrated according to logged data collected over four months. Results showed that for the system analyzed, the energy saving of optimizing the cooling tower fan system was found to be 12–15%, while the energy saving potential of optimizing the condenser water pump with the cooling tower fan was negligible. Additionally, comparing different cooling tower fan control strategies showed that a wet-bulb approach-based cooling tower control strategy was shown to have the highest correlation to the optimized fan speed with an R 2 of 0.924.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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

Cross-database comparisons on the greenhouse gas emissions, water consumption, and fossil-fuel use of plastic resin production and their post-use phase impacts

Resin production and post-use pathways have cross-database discrepancies due to different temporal/geographic representation, technologies, and assumptions. These differences can distort comparisons across alternatives and confound efforts to establish standards based on the plastics’ life-cycle impacts. Thus, this study quantifies the degree and identifies the sources of cross-database discrepancies across four LCA databases (GREET, USLCI, Ecoinvent, and GaBi) for five resin production pathways and three post-use phases. For resin production pathways, all resins showed significant cross-database discrepancy in their global warming impacts: the degree of discrepancy was significant to recommend a consistent choice of database to users any LCA comparisons across different products. For post-use phases, landfill datasets had relatively lower degree of cross-database discrepancy than incineration and mechanical recycling. Different metadata characteristics were the sources of some cross-database discrepancies while other parts could be explained by the original differences in the life-cycle inventory sourced from different producers and plants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Minimizing grid energy consumption in wastewater treatment plants: Towards green energy solutions, water sustainability, and cleaner environment

Wastewater treatment plants (WWTPs) consume significant amount of energy to sustain their operation. From this point, the current study aims to enhance the capacity of these facilities to meet their energy needs by integrating renewable energy sources. The study focused on the investigation of two primary solar energy systems in As Samra WWTP in Jordan. The first system combines parabolic trough collectors (PTCs) with thermal energy storage (TES). This system primarily serves to fulfill the thermal energy demands of the plant by reducing the demands from boiler units, which allows more biogas for electricity generation. The second system is a photovoltaic (PV) system with Lithium-Ion batteries, which directly produces electricity that will be used to cover part of the electrical energy demands of plant. To assess the optimal configuration, two distinct scenarios have been formulated and compared to the current case scenario (SC#1). The first scenario focuses on maximizing the net present value (NPV) and minimizing the levelized cost of electricity (LCOE). The second scenario is centred on minimizing the levelized cost of heat (LCOH). The findings indicate that both scenarios succeeded in reducing the reliance on the grid to a value that reach 1 %. Moreover, they both reduced biogas percentage in energy production from 88 % to approximately 65 % through the integration of the PV system. In terms of thermal demand, SC#2 reduced the reliance on biogas boiler units from 100 % to 25 %, while SC#3 achieved an even more impressive reduction to just 8 %. The best LCOE value was attained in SC#2, at 0.0895 USD/kWh, with an NPV of 10.54 million USD. Conversely, SC# 3 yielded an LCOH value of 0.0432 USD/kWh th compared to 0.0534 USD/kWh th USD for SC#2. In conclusion, despite their relatively high capital and operating costs, SC#2 and SC#3 managed to substantially decrease the annual electricity expenditure from approximately 2 million USD to 86,000 USD and 0 USD, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit

Here, this paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.

Autonomie↗
DOE OSTI2019

Evaluation of Gas Formation and Consumption Driven by Crossover Effect in High-Voltage Lithium-Ion Batteries with Ni-Rich NMC Cathodes

Gas formation during lithium-ion battery (LIB) cycling impacts the stability and safety of these batteries, especially for those containing Ni-rich NMC cathodes. In this paper, the cycling performance and gassing behavior of NMC811/graphite full cells with 4.2 and 4.4 V upper cutoff voltages were first compared. We report cells with a 4.2 V upper cutoff voltage had good cycling stability, exhibiting a capacity retention of 96.8% after 100 cycles and generated little gas. On the other hand, cells with a 4.4 V upper cutoff voltage lost over 25% of initial capacity after 100 cycles and generated large amounts of gas in the first 10 cycles. Electrochemical cycling of anode and cathode symmetric cells was implemented to isolate gases formed at the electrode. Gas chromatography–mass spectrometry, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning transmission electron microscopy were used to characterize the gas formation and associated material surfaces and structural properties. It was found that CO2 and fluorinated alkanes were the dominant gases evolved on the cathode side during cycling to 4.4 V. Gas crossover to the anode led to the depletion of gaseous products, which stabilized the cell performance to some extent. However, the growing surface reconstruction layer at the cathode, the thickening of the solid electrolyte interphase layer at the anode, and the gradual depletion of lithium inventory collectively contributed to the continuous capacity loss of full cells cycled to 4.4 V.

25 ENERGY STORAGE↗
DOE OSTI2020

Probabilistic Power Consumption Modeling for Commercial Buildings Using Logistic Regression Markov Chain

The total energy consumed by buildings takes up to 40% of U.S. energy use, in which a large portion is contributed by commercial buildings. Building performance optimization is desirable but requires accurate building models with uncertainties taken into account. This paper proposes a novel probabilistic modeling method using Logistic Regression Markov Chain (LRMC). The LRMC model enhances the performance of traditional Markov Chain (MC) models by adopting time-variant transition matrices calibrated using logistic regression with exogenous inputs. Compared with existing building models, the proposed model produces accurate multi-step modeling results with full probability distribution. The proposed probabilistic building model is tested using actual commercial building measurements and modeling performance is evaluated with two probabilisitc metrics. The results show that the LRMC model has higher accuracy than traditional MC model and Logistic Regression (LR) model in that it yields lower error scores under both evaluation metrics.

Building modeling↗
DOE OSTI2014

Reducing Idle Power Consumption in Office Spaces Saves U.S. Navy in Energy Costs (Fact Sheet)

As part of a two-year project to demonstrate energy efficiency measures, renewable energy generation, and energy systems integration, the National Renewable Energy Laboratory (NREL) has identified advanced plug load controls as a promising technology for reducing energy use and related costs in the U.S. Navy's Naval Facilities Engineering Command (NAVFAC) office spaces.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Measurement and Characterization of Haswell Power and Energy Consumption

This is a presentation intended for the NMC seminar. Its primary objective was to understand power-performance of Haswell EP for HPC; test four benchmark programs on PowerEdge R730 with control knobs; as well as test more applications, use more compute nodes, examine the effects of control knobs, and implement power capping.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗