The Annual Coal Report (ACR) provides annual data on U.S. coal production, number of mines, productive capacity, recoverable reserves, employment, productivity, consumption, stocks, and prices. All data for 2015 and prior years are final. From the Energy Information Administration, U.S. Department of Energy. Internet Archive URL: https://web.archive.org/web/*/http://www.eia.gov/coal/annual/

The Annual Energy Outlook presents longterm annual projections of energy supply, demand, and prices focused on the U.S. through 2050, based on results from EIA's National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internally-consistent sets of assumptions, the results of which are presented as cases. The analysis in AEO2014 focuses on five primary cases: a Reference case, Low and High Economic Growth cases, and Low and High Oil Price cases. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

The Annual Energy Outlook API presents long-term annual projections of energy supply, demand, and prices through 2040. The projections, focused on U.S. energy markets, are based on results from EIA’s National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internally-consistent sets of assumptions, the results of which are presented as cases. The projections cover natural gas, petroleum, coal, electricity and renewable fuels by sector (residential, commercial, industrial, electric generation, and transportation) and by region (census). Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

Note: Updates to this data product are discontinued. The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People's Republic of China. Analysts and policy professionals around the world need information about the rapidly changing Chinese economy, but statistics are often published only in China and sometimes only in Chinese-language publications. This product assembles a wide variety of data items covering agricultural production, inputs, prices, food consumption, output of industrial products relevant to the agricultural sector, and macroeconomic data.

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022). These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data. Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period. Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented. Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region. One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold). The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock. Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

This dataset contains Electricity Supply and Consumption for 2012-2019. Data from United Nations Statistics Division. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes: - Refer back to the original source for numbers estimated by the United Nations Statistics Division (numbers with * symbol).- Please refer to the Definitions Section on pages ix to xv for the appropriate product description/ classification, and xvi to xvii for the descriptions of relevant flows.- Supply is calculated as Production plus Imports minus Exports.- EIOU and losses refer to energy industries' own use of electricity, including for pumped storage, and losses.- Total Final Consumption (TFC) refers to the final energy consumption of electricity.

The data from which these carbon-emissions estimates were derived are values of fuel consumed: in billions of cubic feet, for natural gas; in millions of barrels, for petroleum products; and in thousands of short tons, for coal. The resulting emissions estimates are expressed as teragrams of carbon. A teragram is 1012 grams, or 106 metric tons. To convert from carbon to carbon dioxide, multiply by 44/12 (=3.67). Data are available for over 30 different petroleum products, with the exact breakdown varying somewhat from year to year. These products have been treated separately here until the final step of the estimation, at which time CO2 emissions were summed and attributed to liquid petroleum products. These fuel-consumption data are available from the Energy Information Administration of the U.S. Department of Energy. They are published in the Monthly Energy Review, and are available electronically from the Energy Information Administration.

Explore the Expenditure on Gross Domestic Product dataset at constant 2010 prices, including components such as Gross Fixed Capital Formation, Government, Non-Government, Exports of Goods and Services, and more in Saudi Arabia. Access comprehensive GDP data and National Accounts information. Gross Fixed Capital Formation, Government, Non-Government, Exports of Goods and Services, Change in Inventories, Gross Capital Formation, Domestic Final Demand, Govt. Final Consumption Expenditure, Gross Domestic Product, Gross Final Consumption Expenditure, Net exports, Imports of Goods and Services, Private Final Consumption Expenditure, GDP, exports, consumption, imports, National Accounts, change in stock, GDP data Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..2021: Preliminary Data. 

Explore the latest data on expenditure on Gross Domestic Product in Saudi Arabia, including categories such as Gross Fixed Capital Formation, Government Final Consumption Expenditure, and Net Exports. Gain insights into the country's economic performance and trends. Gross Fixed Capital Formation - Government, Gross Fixed Capital Formation - Non-Government, Gross Fixed Capital Formation, Change in Inventories, Gross Capital Formation, Domestic Final Demand, Govt. Final Consumption Expenditure, Gross Final Consumption Expenditure, Net exports, Exports of Goods & Services, Imports of Goods & Services, Expenditure on Gross Domestic Product, Private Final Consumption Expenditure, GDP, exports, consumption, imports, National Accounts, GDP data Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..2021: Preliminary  Methodology Link: https://www.stats.gov.sa/en/node/9773,https://www.stats.gov.sa/en/node/9774

This webpage contains sources of (1) Data tables of federal agency energy and water consumption; (2) Interactive graphics associated with most data tables; (3) Energy costs by end-use sector and efficiency investment information; (4) Progress toward key goals outlined in the National Energy Conservation Policy Act, as amended (42 U.S.C. 8253-8258); Energy Policy Act of 2005 (42 U.S.C. 15852); (5) Historical data tables of agency energy use and costs by facility and mobility sectors by energy type are also available for fiscal year (FY) 1975 through FY 2020

This product summarizes fertilizer consumption in the United States by plant nutrient and major fertilizer products—as well as consumption of mixed fertilizers, secondary nutrients, and micronutrients—for 1960 through the latest year for which statistics are available. The share of planted crop acreage receiving fertilizer, and fertilizer applications per receiving acre (by nutrient), are presented for major producing States for corn, cotton, soybeans, and wheat (data on nutrient consumption by crop start in 1964). Fertilizer farm prices and indices of wholesale fertilizer prices are also available.

Online web mapping tool for visualization and simple analysis of Earth-energy data files from public and DOE related sources. Geocube allows users to upload and visualize their own datasets but also comes preloaded with individual spatial datasets as well as spatial data collections that align to topical themes.

Data on water utilities for 151 national jurisdictions, for a range of years up to and including 2017 (year range varies greatly by country and utility) on service and utility parameters (Benchmark Database) and Tariffs for 211 juristictions (Tariffs database). Information includes cost recovery, connections, population served, financial performance, non-revenue water, residential and total supply, total production. Data can be called up by utility, by group of utility, and by comparison between utilities, including the whole (global) utility database, enabling both country and global level comparison for individual utilities. Data can be downloaded in xls format.

Total and marginal budget shares and income and price elasticities are estimated, using 2005 ICP data, for nine broad consumption groups and eight food subgroups across 144 countries.

The Modernising Energy Data Access (MEDA) competition was set up by Innovate UK and the Modernising Energy Data group to help develop the concept of a Common Data Architecture (CDA) for the Energy Sector. One of the main goals of the Common Data Architecture is to improve data sharing across the energy sector and make data more interoperable across organisations. Energy Consumption is one of the most sought after datasets needed by the organisations that we have worked with throughout a variety of the Modernising Energy Data projects, and although getting a household level of this information comes against GDPR challenges and is therefore non-accessible for the vast majority of organisations, breaking consumption down into smaller areas can be hugely beneficial for gaining insights into how energy is consumed within the UK. We have amalgamated Gas and Electricity consumption per Lower Layer Super Output Area (LSOA) which is available to download via file transfer, or via API

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. This submission includes the Energy Model from the RSF Systems Model. Comparing actual performance (metered) with expected performance (modeled) is key to understand corrective actions to ensure performance as originally intended. The Energy Model in this submission was made to model energy usage in the RSF and was compared to actual metered data to verify the model. Measured data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

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. This submission includes the Measured Energy Data from the RSF Systems Model. The data includes a csv with measured data from NREL's Research and Support Facility (RSF) for 2011. The dataset contains hourly data for: Total Cooling (kW)- Total Heating (kW)- Total Mechanical (kW)- Total Lighting (kW)- Total Plug Loads (kW)- Total Data Center (kW)- Total Building (kW)- PV (kW)- Building Net (kW). Energy Model data and Weather data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

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. This submission includes the Weather data form the RSF systems energy model. The weather data was used to contextualize and compare energy use data like heating and cooling. The weather data was also used by the RSF Energy Model to model the energy use of the RSF. Energy Model data and Measured Energy data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

Data and statistics on natural gas prices, exploration and reserves, production, imports and exports, storage, pipelines, and consumption. Data released on a weekly, monthly and annual basis. International data on natural gas production, consumption, imports and exports, CO2 emissions, and reserves.

EIA data on domestic natural gas prices, exploration and reserves, production, imports and exports, storage, pipelines, and consumption. Data released on a weekly, monthly and/or annual basis. The International Energy Data API provides international natural gas data: http://www.eia.gov/beta/api/qb.cfm?category=1505864 Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

Data and statistics on natural gas prices, exploration and reserves, production, imports and exports, storage, pipelines, and consumption. Data released on a weekly, monthly and annual basis. International data on natural gas production, consumption, imports and exports, CO2 emissions, and reserves.

* In this review, primary energy comprises commercially traded fuels, including modern renewables used to generate electricity. ^ Less than 0.05. Notes:Oil consumption is measured in million tonnes; other fuels in million tonnes of oil equivalent.

Official USDA data on production, supply, and distribution of agricultural commodities for the United States and key producing and consuming countries.

The Bundesnetzagentur's electricity market information platform "SMARD" has been online since 2017. SMARD is an abbreviation of the German term "Strommarktdaten", which translates to electricity market data. Data that is published on the SMARD website gives an up-to-date overview of what is happening on the electricity market. Articles with background information describe important electricity market terms and relationships.

The API provides data back to 1990 and projections annually, monthly, and quarterly for 18 months. Summarizes the outlook for demand, supply and prices for petroleum, natural gas, electricity and coal as well as projections of carbon dioxide emissions from the production of fossil fuels, and a discussion of price forecast uncertainty. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

ERS analysts track U.S. and international sugar and sweetener production, consumption, and trade. They also monitor and analyze U.S. sweetener policy and events that affect the domestic, Mexican, and other international sweetener markets.

This data provides daily accumulated water usage volumes supplied from the 10 Melbourne Water storages. This data is collected using more than 120 Flow Monitoring devices across Melbourne Water's Water Transfer Pipe Network, linked to the Telemetry System. Melbourne Water collects and stores observations at six minute intervals. This data summarises the usage volume supplied by Melbourne Water over a 24 hour period (8am to 8am). Water recipients include 3 metro water retailer companies and 5 regional water authorities. This data set is best used in long and short term water demand analysis.NOTE: Whilst every effort has been taken in collecting, validating and providing the attached data, Melbourne Water Corporation makes no representations or guarantees as to the accuracy or completeness of this data. Any person or group that uses this data does so at its own risk and should make their own assessment and investigations as to the suitability and/or application of the data. Melbourne Water Corporation shall not be liable in any way to any person or group for loss of any kind including damages, costs, interest, loss of profits or special loss or damage, arising from any use, error, inaccuracy, incompleteness or other defect in this data.

This data product contains statistics on wheat-including the five classes of wheat: hard red winter, hard red spring, soft red winter, white, and durum-and rye. Includes data published in the monthly Wheat Outlook and previously annual Wheat Yearbook. Data are monthly, quarterly, and/or annual depending upon the data series. Most data are on a marketing year basis, but some are calendar year.