SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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Results for “Electrical load profile”
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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SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Battery Electrolyte Design for Electric Vertical Takeoff and Landing (eVTOL) Platforms
Here, the development of robust and high-performance battery systems is crucial for the advancement of Electric Vertical Takeoff and Landing (eVTOL) vehicles for urban air mobility. This study evaluates the performance of different lithium-ion battery chemistries under Electric Vertical Takeoff and Landing (eVTOL) load profiles. The actual flight data coupled with physical models is used to create discharge profiles for testing on developed lithium-ion cells for eVTOLs. The performance of a standard liquid electrolyte (1.2 M LiPF 6 in EC:EMC), labeled Gen-2, is benchmarked and compared with a fast-charging electrolyte (1.2 M LiFSI in EC:EMC), labeled XFC. Cell analysis involves the use of various techniques, such as impedance spectroscopy, polarization curves, and capacity retention measurements. Capacity retention is stable for both systems over 500 cycles, but unique discharge capacity trends are observed for different mission segments. During the initial takeoff hover stages, Gen-2 electrolytes experience substantial voltage fade, while XFC electrolytes maintain consistent behavior. In general, the Gen-2 electrolyte demonstrated lower discharge overpotentials and higher decay during cycling compared to the XFC electrolyte. This work highlights the complexity of eVTOL battery behavior and provides insights into battery system design, contributing to the advancement of battery energy storage solutions for urban air mobility.
25 ENERGY STORAGE↗
GSE Daily Energy Consumption for Each Airport
This dataset contains GSE daily energy consumption for each airport.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Charger Requirements for GSE at Each Airport
This dataset contains the required number of chargers for GSE at each airport under six charging scenarios: (1) charging when the battery state of charge is insufficient for the next service using 40-kW chargers (S1); (2) charging when the battery state of charge is insufficient for the next service using 20-kW chargers (S2); (3) charging starts immediately after each GSE completes a service task using 40-kW chargers (S3); (4) charging starts immediately after each GSE completes a service task using 20-kW chargers (S4); (5) charging during off-peak hours using 40-kW chargers (S5); and (6) charging during off-peak hours using 20-kW chargers (S6).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
GPS Locations for Each Airport
This dataset contains GPS locations for each airport.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies
Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Field Performance of Commercial Building Load Flexibility Using Model Predictive Control
Model Predictive Control (MPC) applied to buildings is starting to see some commercial adoption by companies. However, it is hard to estimate if relative energy cost savings are enough to justify the cost of MPC implementation with few reported demonstrations. In small commercial and residential buildings, a one size-fits-all solution can help reduce implementation costs, while in very large buildings or districts the potential energy cost savings magnitude can cover more tailored solutions. This estimation becomes harder for medium to large commercial buildings, where a one-size-fits-all solution cannot be adopted and potential energy cost savings might not be sufficient to cover a tailored solution. Therefore, value propositions in addition to energy efficiency alone can make MPC technology more attractive through additional energy cost savings. One such value proposition is load shifting in response to dynamic electricity prices. On this aspect, MPC is a key technology to unlock building thermal mass for energy flexibility in response to electric grid conditions. This study shows the experimental results of MPC control of an office building in Berkeley, where different dynamic electricity price profiles were used in the MPC objective function to shift the building load and to calculate hypothetical electricity costs. Results show potential 50% cost savings with respect to the existing controller with the dynamic price scenario.
Zanetti, Ettore↗
Putting Our Industry's Data to Work: A Case Study of Large-Scale Data Aggregation: Preprint
With increasing deployment of Advanced Metering Infrastructure (AMI), Building Automation System (BAS) controls, Internet of Things (IoT) network devices, and data-driven evaluation, measurement, and verification studies, the building sector is currently generating a staggering amount of energy-related data. In the right hands, these data sets can contribute to increased comfort and energy savings for building occupants and a more reliable electrical grid; however, due to a combination of factors, including significant privacy concerns, much of the data that are presently generated and stored are not used outside of basic operational applications. In the past year, our team has dedicated over 2,000 person-hours to accessing building energy data for a project funded by the U.S. Department of Energy (DOE) Building Technologies Office. We sought whole-building or end-use (e.g., lighting) timeseries data at the individual-building or equipment level where possible or aggregated information, such as timeseries averages and quartiles by building type (e.g., office, retail, hospital), where sharing individual building information was not an option. We are additionally working with IoT and BAS data sets to derive information important to the project. We have assembled an extensive data set that will enable the development of publicly available end-use load profiles to benefit the U.S. building and electricity industries. Here we present an overview of the data set that we have assembled to date, the motivators and approaches that got us here, and the lessons we learned through our efforts. We also discuss work underway that presents additional options for future data access.
building energy data↗