Search NASA⌕ Search

SEARCH · Search NASA

Results for “renewable energy forecasting”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Abstract Expansion of distributed solar photovoltaic (PV) and natural gas‐fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas‐fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania–New Jersey–Maryland (PJM) Interconnection in the United States over the period 2008–2018. We developed an empirical dynamic panel data model using the system‐generalized method of moments (system‐GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions‐level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost‐related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non‐wire alternative policy technique. This article is categorized under: Photovoltaics > Economics and Policy Fossil Fuels > Climate and Environment Energy Systems Economics > Economics and Policy

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Wave and Offshore Wind Resource in the U.S. Pacific Ocean Minor Outlying Islands

Coastal environments such as islands have unique opportunities for renewable energy resources. This work explores the wave and offshore wind energy potential for the U.S. Pacific Ocean Minor Outlying Islands, including Baker Island, Howland Island, Jarvis Island, Johnston Atoll, Kingman Reef, Palmyra Atoll, and Wake Island. A numerical wave model based on WAVEWATCH III and SWAN (both are NWS / NOAA models) was developed, validated, and executed to generate a 32-year hindcast dataset suitable for resource characterization for each island. A complementary offshore wind resource characterization is provided for the same time period based on the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Each island has five netcdf files: 1. wave_[island]_annual_J_map.nc: the data used to generate the map of annual averaged omnidirectional wave power (J [kW/m]) 2. wave_[island]_100m_IEC: six IEC parameters at 100m isobath from 1979/01/01 to 2020/12/31, output hourly 3. wave_[island]_5km_IEC: six IEC parameters at 5km from the shoreline 1979/01/01 to 2020/12/31, output hourly 4. wind_island_100mASL_windspeed_timeseries.nc: wind speed timeseries from ERA5 at 100 m above sea level within a 60 km radius of the island (m/s) 1979/01/01 to 2010/12/31, output hourly 5. wind_island_100mASL_winddirection_timeseries.nc: wind direction timeseries from ERA5 at 100 m above sea level within a 60 km radius of the island (degrees) 1979/01/01 to 2010/12/31, output hourly

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data---including electric line utilization, nodal price, and available generation flexibility---in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation: Preprint

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data—including electric line utilization, nodal price, and available generation flexibility—in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Integrated Distribution Planning

The contemporary distribution planning landscape is comprised of an increasing number of factors that require integration into the engineering of the modern electric grid. Expectations for electric utilities to accommodate heightened awareness of stakeholders' interest in things like decarbonization, resilience and equity are growing. As these interests are formed into objectives, many jurisdictions will experience increasing levels of load modifying technologies like DER, building and industrial electrification and electric vehicles which prove not only to challenge the capabilities of the grid; but the processes by which planning for it is traditionally done. Other related factors that strain the conventional distribution planning mold are the swelling amount and sources of data associated with these technologies and the need it creates for improved capabilities in the processes and tools that manage it. As the complexity of the distribution system expands, so will the distribution system's effects on the transmission and generation systems that it is a part of. Forecasting distribution system load and DER are examples of areas where this complexity will manifest, and harmonizing distribution forecasting with transmission and generation forecasting requires higher amounts of intentionality as these typically separate processes become a solitary one. Of course, core activities do not cease as a utility begins to integrate these other factors, and in this webinar we explore specifics of how distribution planning can be expected to evolve as progress towards Integrated Distribution System Planning is made.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Evolution of Scenario Generation Capabilities in the ExaSGD Project

High-fidelity renewable energy scenarios and rare-event high-impact contingencies are essential for modeling operations and infrastructure expansion of the next generation power grids using exascale computing resources. As the ExaSGD project progressed, our capabilities for generating scenarios for modeling wind power output at multiple wind farms significantly improved, evolving from the use of independent random perturbations to importance sampling-based techniques capturing representative spatial-temporal relations. Contingency generation also improved, evolving from standard N-1 techniques to probabilistic models of infrastructure damage resulting from extreme weather events. We review the developments in the realistic intermittent-energy scenario forecasting and extreme-event contingency creation as the ExaSGD project progressed and discuss the future work in these areas.

economic dispatch↗
DOE OSTI2021

A Data-Driven Multi-Period Importance Sampling Strategy for Stochastic Economic Dispatch

Power systems with high penetrations of renewable energy (e.g., wind power) require sophisticated approaches to optimize system performance due to uncertainty in short-term system generation capacity. In this paper, we combine a data-driven analog scenario selection method with importance sampling to create a novel scenario construction approach for two-stage stochastic economic dispatch problems with a large number of wind farms on a network. The proposed method produces scenarios with realistic physics by finding high-fidelity analogs that can describe future states of the system. We show how to extend this method to multi-period operations and demonstrate the effectiveness of this technique by simulating economic dispatch operations on a synthetic test system over the course of a week.

data-driven forecasting↗