U.S. Monthly Renewable Energy Consumption by Source and Sector (1973-2024)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2F0fe60a09cda8f60e446422f6721e68f5%2Frenewable%20energy%20consumption%20flag.png?generation=1715139420693463&alt=media" alt=""> This dataset provides monthly data on renewable energy consumption in the United States from January 1973 to December 2024, broken down by energy source and consumption sector. The data is sourced from the U.S. Energy Information Administration (EIA).

Renewable energy has become an increasingly important part of the U.S. energy mix in recent years as the country seeks to reduce its greenhouse gas emissions and dependence on fossil fuels. This dataset allows for detailed analysis of renewable energy trends over time and across different sectors of the economy.

IMPORTANT: Dataset Info

• Every entry that has a value of 0 means that the datapoint was either "Not Available," "No Data Reported," or "Not Meaningful"
• You most likely want to exclude the column titled Total Renewable Energy from your comparative analysis across fuel types as it represents the sum of the others

Columns

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 complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

This dataset provides comprehensive monthly and annual electric power operational data from the U.S. Energy Information Administration (EIA). It covers the period from 2015 to 2024 and includes detailed metrics on electric power generation, consumption, costs, and emissions. The dataset is designed to support analysis and research into the efficiency and environmental impact of electric power operations across various states and sectors in the United States.

Column Description

1. period: The month and year of the data (e.g., 2024-05).
2. location: The state abbreviation (e.g., MI for Michigan, LA for Louisiana).
3. stateDescription: The full name of the state (e.g., Michigan, Louisiana).
4. sectorid: An ID representing the sector (e.g., 2).
5. sectorDescription: A description of the sector (e.g., IPP Non-CHP).
6. fueltypeid: An ID representing the fuel type (e.g., NGO, NG, HYC, OBW).
7. fuelTypeDescription: A description of the fuel type (e.g., natural gas & other gases, natural gas, conventional hydroelectric, biomass).

State-level data on all energy sources. Data on production, consumption, reserves, stocks, prices, imports, and exports. Data are collated from state-specific data reported elsewhere on the EIA website and are the most recent values available. Data on U.S. territories also available.

United States Electricity Consumption data was reported at 10.243 kWh/Day bn in Mar 2025. This records a decrease from the previous number of 11.765 kWh/Day bn for Feb 2025. United States Electricity Consumption data is updated monthly, averaging 9.940 kWh/Day bn from Jan 1991 (Median) to Mar 2025, with 411 observations. The data reached an all-time high of 13.179 kWh/Day bn in Jul 2024 and a record low of 7.190 kWh/Day bn in Apr 1991. United States Electricity Consumption data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under Global Database’s United States – Table US.RB004: Electricity Supply and Consumption. [COVID-19-IMPACT]

Monthly data since January 1973 and annual data since 1949 on U.S. primary and total energy consumption by end-use sector (residential, commercial, industrial, transportation) and electric power sector.

Electricity consumption in the United States totaled ***** terawatt-hours in 2024, the highest value in the period under consideration. Figures represent energy end use, which is the sum of retail sales and direct use of electricity by the producing entity. Electricity consumption in the U.S. is expected to continue increasing in the coming years. Which sectors consume the most electricity in the U.S.? Consumption has often been associated with economic growth. Nevertheless, technological improvements in efficiency and new appliance standards have led to a stabilizing of electricity consumption, despite the increased ubiquity of chargeable consumer electronics. Electricity consumption is highest in the residential sector, followed by the commercial sector. Equipment used for space heating and cooling account for some of the largest shares of residential electricity end use. Leading states in electricity use Industrial hub Texas is the leading electricity-consuming U.S. state. In 2023, the southwestern state, which houses major refinery complexes and is also home to over ** million people, consumed almost ****terawatt-hours. Florida and California followed in second and third, with an annual consumption of approximately *** terawatt-hours and 240 terawatt-hours, respectively.

Estimated industrial manufacturing agriculture construction and mining energy estimated by North American Industrial Classification System NAICS code county and fuel type for 2014. Additional disaggregation by end use e.g. machine drive process heating facility lighting is provided for manufacturing agriculture and mining industries. Estimation approach is described in detail in the data_foundation folder here https//github.com/NREL/Industry-Energy-Tool/.

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.

US Electricity Prices and Sales by State, Sector, and Year

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2Fdba0dac3571f37e79f2891a6ffd80d5c%2Fus%20electric%20flag.png?generation=1712518711362350&alt=media" alt=""> This comprehensive dataset offers a detailed look at the United States electricity market, providing valuable insights into prices, sales, and revenue across various states, sectors, and years. With data spanning from 2001 onwards to 2024, this dataset is a powerful tool for analyzing the complex dynamics of the US electricity market and understanding how it has evolved over time.

The dataset includes eight key variables: | Column Name | Description | |-------|-------| | year | The year of the observation | | month | The month of the observation | | stateDescription | The name of the state | | sectorName | The sector of the electricity market (residential, commercial, industrial, other, or all sectors) | | customers | The number of customers (missing for some observations) | | price | The average price of electricity per kilowatt-hour (kWh) in cents | | revenue | The total revenue generated from electricity sales in millions of dollars | | sales | The total electricity sales in millions of kilowatt-hours (kWh) |

By providing such granular data, this dataset enables users to conduct in-depth analyses of electricity market trends, comparing prices and consumption patterns across different states and sectors, and examining the impact of seasonality on demand and prices.

One of the primary applications of this dataset is in forecasting future electricity prices and sales based on historical trends. By leveraging the extensive time series data available, researchers and analysts can develop sophisticated models to predict how prices and demand may change in the coming years, taking into account factors such as economic growth, population shifts, and policy changes. This predictive power is invaluable for policymakers, energy companies, and investors looking to make informed decisions in the rapidly evolving electricity market.

Another key use case for this dataset is in investigating the complex relationships between electricity prices, sales volumes, and revenue. By combining the price, sales, and revenue data, users can explore how changes in prices impact consumer behavior and utility company bottom lines. This analysis can shed light on important questions such as the price elasticity of electricity demand, the effectiveness of energy efficiency programs, and the potential impact of new technologies like renewable energy and energy storage on the market.

Beyond its immediate applications in the energy sector, this dataset also has broader implications for understanding the US economy and society as a whole. Electricity is a critical input for businesses and households across the country, and changes in electricity prices and consumption can have far-reaching effects on economic growth, competitiveness, and quality of life. By providing such a rich and detailed portrait of the US electricity market, this dataset opens up new avenues for research and insights that can inform public policy, business strategy, and academic inquiry.

I hope you all enjoy using this dataset and find it useful! 🤗

United States US: Renewable Energy Consumption: % of Total Final Energy Consumption data was reported at 8.717 % in 2015. This records a decrease from the previous number of 8.754 % for 2014. United States US: Renewable Energy Consumption: % of Total Final Energy Consumption data is updated yearly, averaging 5.454 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 8.754 % in 2014 and a record low of 4.089 % in 1994. United States US: Renewable Energy Consumption: % of Total Final Energy Consumption data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Energy Production and Consumption. Renewable energy consumption is the share of renewables energy in total final energy consumption.; ; World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program.; Weighted Average;

📊 Overview

This dataset contains monthly electricity consumption data and related indicators for the 48 contiguous U.S. states from January 1990 to December 2023. The dataset was constructed by merging time-series data from six key U.S. government agencies and includes 35 variables spanning:

Climatological and weather data Demographics Economic indicators Labor force statistics Geographical and urban-rural characteristics Environmental and energy data Electricity production, pricing, and consumption metrics

Each row corresponds to a unique month-year and state combination (48 samples per month-year), totaling 19,584 samples.

🎯 Targets

The dataset includes three target variables for modeling:

REC – Residential Electricity Consumption CEC – Commercial Electricity Consumption IEC – Industrial Electricity Consumption

🔧 Format

Rows: 19,584 (408 per state) Columns: 32 features + 3 target variables Time range: 1990–2023 Frequency: Monthly

Includes state as a categorical feature for multi-state learning

📚 Citation Request

This dataset is associated with the paper titled:

Electricity Demand Prediction Using Data-Driven Models: A Comprehensive Multi-Sector Analysis of Energy Consumption Dynamics (currently under peer review).

If you use this dataset in your research or publication, please cite the forthcoming paper.

📌 The formal citation will be added here once the paper is published. Follow this dataset for updates.

📥 Source Data

Raw data was sourced from:

U.S. Energy Information Administration (EIA) National Oceanic and Atmospheric Administration (NOAA) U.S. Census Bureau Bureau of Labor Statistics Environmental Protection Agency (EPA) U.S. Department of Commerce

All data used is publicly available and has been processed for consistency, completeness, and ease of use in machine learning and statistical modeling.

This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.

Scenario data from the Electrification Futures Study Scenarios of Electric Technology Adoption and Power Consumption for the United States report. Annual projections from 2017 to 2050 of electric technology adoption and energy consumption for five scenarios reference electrification medium electrification high electrification electrification potential and low electricity growth. Each scenario assumes moderate technology advancement as described by Jadun et al. 2017 https//www.nrel.gov/docs/fy18osti/70485.pdf.

⚡ US Electricity Generation & Emissions Dataset

Monthly and yearly electricity data for the United States, available at both national and state levels.
The dataset covers electricity generation (GWh), power generation capacity (MW), emissions from electricity generation (ktCO2e) and carbon intensity of electricity generation (gCO2 per KWh) for all 50 states.

🗂️ Columns

• Country – Country name (United States of America)
• Country code – ISO-3 country code (USA)
• State – U.S. state or territory (e.g., Alabama)
• State code – State abbreviation (e.g., AL)
• State type – Classification (state / territory / federal district)
• Date – Month and year (DD-MM-YYYY)
• Category – Data category (e.g., Electricity generation, Emissions)
• Subcategory – Aggregate grouping (e.g., Aggregate fuel)
• Variable – Energy type or emission type (Clean, Fossil, Renewables, Gas, Hydro, CO₂, etc.)
• Unit – Measurement unit (e.g., %, GWh, tons)
• Value – Recorded value for the variable
• YoY absolute change – Year-on-year absolute change in value
• YoY % change – Year-on-year percentage change in value

📂 Files

• us_monthly_electricity.csv – Full dataset containing monthly and yearly electricity generation and emissions data at both state and national levels.

🚀 Potential Use Cases

• 📊 State-level comparisons of fossil vs renewable energy adoption
• 📈 Trend analysis of electricity generation and emissions over time
• 🌱 Carbon footprint analysis for clean vs fossil energy
• 🤖 Forecasting models for electricity demand and emission reductions
• 🏛️ Policy evaluation of renewable adoption at state and national levels

📜 License

This dataset is released under the CC0 1.0 Universal (Public Domain Dedication) license.

Energy Consumption of United States Over Time

Building Energy Data Book

By Department of Energy [source]

About this dataset

The Building Energy Data Book (2011) is an invaluable resource for gaining insight into the current state of energy consumption in the buildings sector. This dataset provides comprehensive data on residential, commercial and industrial building energy consumption, construction techniques, building technologies and characteristics. With this resource, you can get an in-depth understanding of how energy is used in various types of buildings - from single family homes to large office complexes - as well as its impact on the environment. The BTO within the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy developed this dataset to provide a wealth of knowledge for researchers, policy makers, engineers and even everyday observers who are interested in learning more about our built environment and its energy usage patterns

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides comprehensive information regarding energy consumption in the buildings sector of the United States. It contains a number of key variables which can be used to analyze and explore the relations between energy consumption and building characteristics, technologies, and construction. The data is provided in both CSV format as well as tabular format which can make it helpful for those who prefer to use programs like Excel or other statistical modeling software.

In order to get started with this dataset we've developed a guide outlining how to effectively use it for your research or project needs.

• Understand what's included: Before you start analyzing the data, you should read through the provided documentation so that you fully understand what is included in the datasets. You'll want to be aware of any potential limitations or requirements associated with each type of data point so that your results are valid and reliable when drawing conclusions from them.

• Clean up any outliers: You may need to take some time upfront investigating suspicious outliers within your dataset before using it in any further analyses — otherwise, they can skew results down the road if not dealt with first-hand! Furthermore, they could also make complex statistical modeling more difficult as well since they artificially inflate values depending on their magnitude within each example data point (i.e., one outlier could affect an entire model’s prior distributions). Missing values should also be accounted for too since these may not always appear obvious at first glance when reviewing a table or graphical representation - but accurate statistics must still be obtained either way no matter how messy things seem!

• Exploratory data analysis: After cleaning up your dataset you'll want to do some basic exploring by visualizing different types of summaries like boxplots, histograms and scatter plots etc.. This will give you an initial case into what trends might exist within certain demographic/geographic/etc.. regions & variables which can then help inform future predictive models when needed! Additionally this step will highlight any clear discontinuous changes over time due over-generalization (if applicable), making sure predictors themselves don’t become part noise instead contributing meaningful signals towards overall effect predictions accuracy etc…

• Analyze key metrics & observations: Once exploratory analyses have been carried out on rawsamples post-processing steps are next such as analyzing metrics such ascorrelations amongst explanatory functions; performing significance testing regression models; imputing missing/outlier values and much more depending upon specific project needs at hand… Additionally – interpretation efforts based

Research Ideas

• Creating an energy efficiency rating system for buildings - Using the dataset, an organization can develop a metric to rate the energy efficiency of commercial and residential buildings in a standardized way.
• Developing targeted campaigns to raise awareness about energy conservation - Analyzing data from this dataset can help organizations identify areas of high energy consumption and create targeted campaigns and incentives to encourage people to conserve energy in those areas.
• Estimating costs associated with upgrading building technologies - By evaluating various trends in building technologies and their associated costs, decision-makers can determine the most cost-effective option when it comes time to upgrade their structures' energy efficiency...

This deposit combines data from https://doi.org/10.3886/E146782V1 and https://doi.org/10.3886/E146801V1 to produce files containing the hourly generation, costs, and capacities of virtually all power plants in the lower 48 United States between 1999-2012 for their use in "Data and Code for: Imperfect Markets versus Imperfect Regulation in U.S. Electricity Generation" (https://doi.org/10.3886/E115467V1).

This dataset is about countries per year in the United States. It has 64 rows. It features 4 columns: country, renewable energy consumption, and individuals using the Internet.

The dataset also includes an overview of the state's energy consumption by fuel type, providing a snapshot of Virginia’s energy mix.

Context

This is a nicely formatted version of the US Energy Information Administration's U.S. Electric System Operating Data.

It's broken out by aggregation level: US, Regions, Balancing authorities and Balancing authority subregion. Then within that it's broken out into either region, balancing authority or individual utility.

Then each csv is includes data on - BA-to-BA interchange (suffix ID.H) - Day-ahead demand forecast (DF.H) - Demand (D.H) - Net generation by energy source (NG.SUN.H, NG.COL.H, NG.NG.H etc) - Net generation (NG.H) - Total interchange (TI.H)

Note: .H in the suffix stands for hourly in UTC time.

You can see the full data dictionary in data_dictionary.csv

Raw data

The raw data comes from the EIA's bulk data download facility. It's downloaded using this notebook. And structured using this notebook.