Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe have further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels determine the amount of electricity the average person requires in each country.

The Utility Rate Database (URDB) is a free storehouse of rate structure information from utilities in the United States. Here, you can search for your utilities and rates to find out exactly how you are charged for your electric energy usage. Understanding this information can help reduce your bill, for example, by running your appliances during off-peak hours (times during the day when electricity prices are less expensive) and help you make more informed decisions regarding your energy usage.

Rates are also extremely important to the energy analysis community for accurately determining the value and economics of distributed generation such as solar and wind power. In the past, collecting rates has been an effort duplicated across many institutions. Rate collection can be tedious and slow, however, with the introduction of the URDB, OpenEI aims to change how analysis of rates is performed. The URDB allows anyone to access these rates in a computer-readable format for use in their tools and models. OpenEI provides an API for software to automatically download the appropriate rates, thereby allowing detailed economic analysis to be done without ever having to directly handle complex rate structures. Essentially, rate collection and processing that used to take weeks or months can now be done in seconds!

NREL’s System Advisor Model (formerly Solar Advisor Model or SAM), currently has the ability to communicate with the OpenEI URDB over the internet. SAM can download any rate from the URDB directly into the program, thereby enabling users to conduct detailed studies on various power systems ranging in size from a small residential rooftop solar system to large utility scale installations. Other applications available at NREL, such as OpenPV and IMBY, will also utilize the URDB data.

Upcoming features include better support for entering net metering parameters, maps to summarize the data, geolocation capabilities, and hundreds of additional rates!

The HANPP Collection: Human Appropriation of Net Primary Productivity as a Percentage of Net Primary Productivity represents a map identifying regions in which human consumption of NPP is greatly in excess of production by local ecosystems. Humans appropriate net primary productivity through the consumption of food, paper, wood and fiber, which alters the composition of the atmosphere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services. Net primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems.

Tuvalu’s environment is under pressure: sea-water rise contaminating the soil with salt, direct impact on waste and sewage systems from rising human density contributing to further damage. The 1987 UN Brundlandt report has definitely shown the existing link between environment/ecology and development /economy. Tomorrow’s economy stems from today’s environment. Investing in the quality of soil, avoiding water pollution, protecting natural resources especially energy sources as well as fighting against climate change will largely determine the success of Tuvalu’s development for this new century. The current study concerning renewable energy potential and implementation in Tuvalu is at the crossroad of 2 issues, each with major strategic implications: climate change threats and worldwide oil crises. Given this context, what can renewable energy contribute to Tuvalu’s benefit? Analysis of Tuvalu’s energy consumption reveals the following characteristics: • Tuvalu’s economy is almost totally dependant on oil. Only around 18% comes from local biomass resources, which is not accounted for in official statistics and is not the object of any active policy. • Consumption for transportation: primarily sea transport and recently, road transport, account for over 50% of total current energy consumption. • Prime importance of electricity production: courtesy of a Japanese aid program, an initiative to reinforce production with new diesel generators is slated to be implemented on Funafuti in 2006 continuing Tuvalu’s dependence on imported oil. • The 3rd highest energy consumption, thermal use (cooking, boiling water for drinking, sanitary hot water), is mainly provided by biomass.

The Macquarie Island Station Area GIS Dataset is a topographic and facilities data base covering Australia's Macquarie Island Station and its immediate environs. The database includes all man made and natural features within the operational area of the station proper. Attributes are held for many facilities including, buildings, site services, communications, fuel storage, aeronautical and management zones. The spatial data have been compiled from low level aerial photography, ground surveys and engineering plans. Detail attribution of hydraulic site services includes make, size and engineering plan number.

The dataset conforms to the SCAR Feature Catalogue which includes data quality information.

The data is included in the data available for download from a Related URL below. The data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 25. Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.

Changes have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added. As a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).

This collection contains two datasets: one, data used in TI-City model to predict future urban expansion in Accra, Ghana; and two, residential electricity consumption data used to map intra-urban living standards in Karachi, Pakistan. The TI-City model data are ASCII files of infrastructure and amenities that affect location decisions of households and developers. The residential electricity consumption data consist of average kilowatt hours (kw/h) of electricity consumed per month by ~ 2 million households in Karachi. The electricity consumption data is aggregated into 30m grid cells (count = 193050), with centroids and consumption values provided. The values of the points (centroids), captured under the field "Avg_Avg_Cs", represents the median of average monthly consumption of households within the 30m grid cells.Our project addresses a critical gap in social research methodology that has important implications for combating urban poverty and promoting sustainable development in low and middle-income countries. Simply put, we're creating a low-cost tool for gathering critical information about urban population dynamics in cities experiencing rapid spatial-demographic and socioeconomic change. Such information is vital to the success of urban planning and development initiatives, as well as disaster relief efforts. By improving the information base of the actors involved in such activities we aim to improve the lives of urban dwellers across the developing world, particularly the poorest and most vulnerable. The key output for the project will be a freely available 'City Sampling Toolkit' that provides detailed instructions and opensource software tools for replicating the approach at various spatial scales. Our research is motivated by the growing recognition that cities are critical arenas for action in global efforts to tackle poverty and transition towards more environmentally sustainable economic growth. Between now and 2050 the global urban population is projected to grow by over 2 billion, with the overwhelming majority of this growth taking place in low and middle-income countries in Africa and Asia. Developing evidence-based policies for managing this growth is an urgent task. As UN Secretary General Ban Ki Moon has observed: "Cities are increasingly the home of humanity. They are central to climate action, global prosperity, peace and human rights...To transform our world, we must transform its cities." Unfortunately, even basic data about urban populations are lacking in many of the fastest growing cities of the world. Existing methods for gathering vital information, including censuses and sample surveys, have critical limitations in urban areas experiencing rapid change. And 'big data' approaches are not an adequate substitute for representative population data when it comes to urban planning and policymaking. We will overcome these limitations through a combination of conceptual innovation and creative integration of novel tools and techniques that have been developed for sampling, surveying and estimating the characteristics of populations that are difficult to enumerate. This, in turn, will help us capture the large (and sometimes uniquely vulnerable) 'hidden populations' in cities missed by traditional approaches. By using freely available satellite imagery, we can get an idea of the current shape of a rapidly changing city and create a 'sampling frame' from which we then identify respondents for our survey. Importantly, and in contrast with previous approaches, we aren't simply going to count official city residents. We are interested in understanding the characteristics of the actually present population, including recent migrants, temporary residents, and those living in informal or illegal settlements, who are often not considered formal residents in official enumeration exercises. In other words, our 'inclusion criterion' for the survey exercise is presence not residence. By adopting this approach, we hope to capture a more accurate picture of city populations. We will also limit the length of our survey questionnaire to maximise responses and then use novel statistical techniques to reconstruct a rich statistical portrait that reflects a wide range of demographic and socioeconomic information. We will pilot our methodology in a city in Pakistan, which recently completed a national census exercise that has generated some controversy with regard to the accuracy of urban population counts. To our knowledge this would be the first project ever to pilot and validate a new sampling and survey methodology at the city scale in a developing country. The TI-City data was accessed from institutions responsible for land use and planning in Ghana as well as secondary sources (See the the underlying paper for more https://doi.org/10.1177/23998083211068843). The residential electricity consumption data was provided by K-Electric (KE), the monopoly provider of electricity in Karachi. The data pertains to ~2 million households aggregated into 30m grid cells (see the underlying paper for more https://dx.doi.org/10.2139/ssrn.4154318).

Low specific mass (< 3  kg/kW) in-space electric power and propulsion can drastically alter the paradigm for exploration of the Solar System, changing human Mars exploration from a 3-year epic event to an annual expedition.   A specific mass of ~1 kg/kW can enable 1-year round-trips to Mars, regardless of alignment, with the same launch mass to low Earth orbit (350 mT) estimated by the Mars Design Reference Architecture 5.0 study for a 3-year conjunction mission. Key to achieving such a propulsion capability is the ability to convert, at high efficiency and with only minimal losses rejected as heat via radiators, the energy of charged particle reaction products originating from an advanced fission or aneutronic fusion source directly into electricity conditioned as required to power an electric thruster.  The TWDEC concept accomplishes this by converting particle beam energy into radio frequency (RF) alternating current electrical power, such as can be used to heat the propellant in a plasma thruster.

This project is core to the development of multi-MW power for electric propulsion.  The technology developed will enable high power systems which have specific mass in the low single-digits and which are sun-independent, require no neutron shielding, and produce no radioactive waste.  The power levels and specific mass this technology could provide will, when combined with either high-efficiency Q-thrusters or VASIMR-class plasma thrusters, enable rapid human missions to Mars and beyond.     Project Infusion Path: Low specific mass (a – kg/kWe) in-space electric power and propulsion can drastically alter the paradigm for exploration of the Solar System, changing human Mars exploration from a 3-year epic event to an annual expedition.   An a of ~1 kg/kWe can enable 1-year round-trips to Mars, regardless of opportunity, with the same launch mass to low Earth orbit (350 mT) estimated by the Mars Design Reference Architecture 5.0 study for a 3-year conjunction mission. Key to achieving such a propulsion capability is the ability to convert, at high efficiency and with only minimal losses rejected as heat via radiators, the energy of charged particle reaction products originating from an aneutronic fusion source directly into electricity conditioned as required to power an electric thruster. The TWDEC concept (originally conceived in Japan in the 1990’s for terrestrial fusion applications) accomplishes this by converting particle beam energy into radio frequency (RF) alternating current electrical power, such as can be used to heat the propellant in a VASIMR-class plasma thruster. In a more advanced concept (explored in a 2012 Phase 1 NASA Innovative Advanced Concepts (NIAC) project), the TWDEC could also be utilized to condition the particle beam such that it may transfer directed kinetic energy to a target propellant plasma for the purpose of increasing thrust and optimizing the specific impulse.  While other government agencies and/or industry partners are pursuing aneutronic fusion reactors and plasma propulsion, NASA JSC is the only entity advancing this core energy conversion technology. With successful development of this system by NASA and its partners, an intermediate NASA infusion step would demonstrate megawatt-class aneutronic fusion, TWDEC, and electric propulsion (e.g., Q-thruster, VASIMR) systems on robotic missions to the Jovian moons.  Human vehicle system development would then integrate such systems into the “ultimate” NASA application:  sustainable, routine human exploration of Mars and, with successful Q-thruster development, beyond.

Project Infusion Path:

Low specific mass (a – kg/kW_e) in-spac

The Commercial Occupancy Dataset (COD) is a high-resolution long-term dataset of occupancy traces in a commercial office building spanning 9 months and covering room-level occupancy for three different spaces (two conference rooms and one open-plan space) containing more than 90,000 enter/exit events over this time period. Occupancy data in a building contains rich spatial-temporal information about the users and their usage of the space and facilities. However, obtaining accurate occupancy data is a very challenging task due to the limitation of existing sensing technologies. A novel depth-imaging based solution to estimate occupancy counts was deployed in four doorways of an office building to generate the dataset. We envision the dataset being used for diverse applications such as building energy simulation, occupancy modeling and human-in-the-loop HVAC control which enhance energy efficiency and human comfort.

Each folder in the dataset represents data from a single building. Inside the folder there will be separate comma-separated value (CSV) files, one for each monitored room within the building. Each CSV file contains an entry for every entrance and exit events that was estimated by the sensor(s) corresponding to the room in question. In turn, each one of these entries in the dataset (i.e., each line in the file) contains three fields in this order: date (m/dd/yy), time (HH:MM:SS) and occupancy count.

GeoTIFF of Wind Power Class values for the state of Alaska. This map shows the annual average wind power estimates at a height of 50 meters. It is a combination of high resolution and low resolution datasets produced by NREL and other organizations. The data was screened to eliminate areas unlikely to be developed onshore due to land use or environmental issues. In many states, the wind resource on this map is visually enhanced to better show distribution on ridge crests and other features.