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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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This case study describes how Commonwealth Edison (ComEd) and the city of Chicago worked together to develop a way for commercial building owners to get access to whole-building energy data more efficiently. This would enable building owners to comply with local energy benchmarking ordinances.
The case study describes how Eversource and the cities of Boston and Cambridge, Massachusetts, worked together to develop a way for commercial building owners to get access to whole-building energy data more efficiently. This would enable building owners to comply with local energy benchmarking ordinances.
The Open Energy Data Initiative Solar Systems Integration (OEDI SI) Data and Modeling Platform offers a comprehensive set of use cases tailored for power systems analysis. Each use case is centered around a specific power system analysis problem, supported by composite input data and reference algorithms. These composite input datasets are meticulously assembled using OEDI SI's data preprocessing tools, which integrate raw data from various sources. The primary objectives of the OEDI SI Platform include facilitating access to composite input data through widely accepted input/output formats and verified results. This accessibility enables power system network researchers and developers to validate their algorithms and showcase their applications' capabilities to the broader community. Moreover, the platform strives to promote reproducible, robust, replicable, and generalizable solar systems integration research.
This case study describes how Rocky Mountain Power, Questar, and Salt Lake City worked together to develop a way for commercial building owners to get access to whole-building energy data more efficiently. It details the use of a stakeholder engagement forum to enable business owners to comply with local energy benchmarking ordinances.
This case study spotlights Washington, D.C.'s successful implementation of DOE’s Unique Building ID (UBID) tool to leverage building-related datasets in support of their sustainability policies.
The National Renewable Energy Laboratory (NREL) has developed OpenEI.org, a public, open, data-sharing platform where consumers, analysts, industry experts, and energy decision makers can go to boost their energy IQs, search for energy data, share data, and get access to energy applications. The free site blends elements of social media, linked open-data practices, and MediaWiki-based technology to build a collaborative environment for creating and sharing energy data with the world. The result is a powerful platform that is helping government and industry leaders around the world define policy options, make informed investment decisions, and create new businesses.
Report summarizes Federal sector needs and commercially available energy data management systems.
High-quality renewable energy resource data and other geographic information system (GIS) data are essential for the transition to a clean energy economy that prioritizes local resources, improves resiliency, creates jobs, and promotes energy independence. These data are crucial for making informed decisions—ranging from policy and investment decisions to reliable power sector planning. Decisions that are data-driven reflect appropriate ambition, maximize cost-effectiveness, and enable successful implementation of renewable energy investments. This guide is intended to support policymakers and planners, as well as technical experts, consultants, and academics in incorporating improved data and analysis into renewable energy decision-making.
Energy infrastructure can both induce and suffer risks from wildfires ranging from direct damage to energy assets such as substations and power lines to Public Safety Power Shutoffs. Recent wildfire events underscore the need for data-driven approaches that help states and utilities proactively plan for wildfire risk. Existing national tools such as Federal Emergency Management Agency (FEMA)’s National Risk Index (NRI) are valuable for community hazard planning. However, they are less suited for energy infrastructure, as they emphasize population and building exposure rather than system vulnerabilities. In this paper, we explore relationships between energy and wildfire data and present a Wildfire-Energy State Prioritization Index (WESPI). Our methodology combines data from the US Forest Service’s Fire Simulation (FSIM) dataset with energy resilience metrics, historical fire incidents, and geospatial data on transmission lines and fire stations. Correlation analyses suggest that FSIM burn probability is more strongly associated with power outage metrics (ρ = 0.32) than NRI wildfire frequency, and counties with a greater density of fire stations experience more frequent, but less intense wildfires. We further leverage data for burn probability, transmission line density, and fire station density to develop a Wildfire-Energy State Prioritization Index (WESPI) to highlight counties where wildfire hazard, infrastructure exposure, and limited suppression capacity converge. The index provides a consistent, scalable framework for state energy offices and utilities to screen counties for vegetation management, optimization of outage management system deployment, and to inform wildfire mitigation plans.
Buildings account for over 40% of the world’s energy consumption and are therefore a key contributor to a country’s energy as well as carbon budget. Understanding how buildings use energy is critical to understanding how related policies may impact energy use. Data enables decision making, and good quality data arms consumers with the tools to compare their energy performance to their peers, allowing them to differentiate their buildings in the real estate market on the basis of their energy footprint. Good quality data are also essential for policy makers to prioritize their energy saving strategies and track implementation. The United States’ Commercial Building Energy Consumption Survey (CBECS) is an example of a successful data framework that is highly useful for governmental and nongovernmental initiatives related to benchmarking energy forecasting, rating systems and metrics, and more. The Bureau of Energy Efficiency (BEE) in India developed the Energy Conservation Building Code (ECBC) and launched the Star Labeling program for a few energy-intensive building segments as a significant first step. However, a data driven policy framework for systematically targeting energy efficiency in both new construction and existing buildings has largely been missing. There is no quantifiable mechanism currently in place to track the impact of code adoption through regular reporting/survey of energy consumption in the commercial building stock. In this paper we present findings from our study that explored use cases and approaches for establishing a commercial buildings data framework for India.
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Heat pumps offer a great low carbon emission method to heat homes. Thermalize Juneau 2021 was a clean energy campaign that helped homeowners in Juneau, Alaska install heat pumps into their homes. Juneau is located near the climactic limit of many heat pumps, and we were interested in how well the heat pumps can function in cold climates. This was done by using Sense meters to remotely monitor the energy usage of 10 homes with heat pumps. The Sense meters are small devices that connect to the electrical panel and monitor the energy use of multiple appliances through machine learning. First, we recorded the energy usage of a heat pump in the lab using both the Sense meter and the existing lab datalogger. Both recorded very similar energy usage data which confirmed that the Sense meter can accurately measure the fluctuations in the heat pump energy use. Next, we looked at the energy data from the Sense meters in the homes with heat pumps and paired it with local weather data to see how well the heat pumps functioned in the cold weather.
The design and implementation of a computerized data base created to support the DSN Energy Conservation Project with data relating to energy use at Goldstone Deep Space Communications Complex are described. The results of development work to date, are presented along with work currently in progress or in the planning stage.
Conference paper for West Coast Energy Management Congress regarding the Federal Building Metering Guidance, subsequent metering implementation plans, and the next steps for Federal agencies.
The Nuclear Data Team has released updated continuous-energy neutron data files for: 1) hydrogen, and 2) S (α; β) (thermal scattering) on SiO 2 . A list of new ZAIDs and the data that is updated (Old ZAID) is given in Table 1. The old data are still accessible, but are not the default.
A new Web-based tool enables users to evaluate the site-specific performance of various building technologies, support more effective financial analyses, and make better-informed procurement decisions. By improving stakeholders’ confidence in performance, the Technology Performance Exchange™ (TPEx™) is streamlining access to foundational energy performance data and helping many viable energy efficiency projects move from the drawing board to the implementation stage.
Miscellaneous and electronic loads (MELs) comprise an increasing share of building energy consumption. Large-scale data collection is needed to inform meaningful energy reduction strategies because of the diversity of MELs and our lack of understanding about how people use them. Traditional methods of data collection, however, usually incur high labor and metering equipment expenses. As an alternative, this paper investigates the feasibility of crowdsourcing data collection to satisfy at least part of the data collection needs with acceptable accuracy. We assessed the reliability and accuracy of crowd-sourced data by recruiting 18 volunteers and testing our crowdsourcing protocol. The protocol asked volunteers to perform measurement tasks for three MELs devices of increasing complexity 1) record power meter and MELs product characteristics, 2) identify and measure all power modes available, and 3) report the measured power. Volunteers performed reasonably well for devices with functionalities with which they were familiar, but many could not correctly identify all available power modes in complex devices. Accuracy may improve when participants measure the power used by familiar devices in their home, or by providing more specific instructions, e.g. videos. Furthermore, crowdsourcing data collection from individual homeowners has the potential to generate valuable information about MELs energy use in homes when integrated with existing programs such as Home Energy Saver and Building America.