Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

Displays several units of energy consumption for households, businesses, and industries in the City of Chicago during 2010. Electric The data was aggregated from ComEd and Peoples Natural Gas by Accenture. Electrical and gas usage data comprises 88 percent of Chicago's buildings in 2010. The electricity data comprises 68 percent of overall electrical usage in the city while gas data comprises 81 percent of all gas consumption in Chicago for 2010. Census blocks with less than 4 accounts is displayed at the Community Area without further geographic identifiers. This dataset also contains selected variables describing selected characteristics of the Census block population, physical housing, and occupancy.

🏡 Household Energy Consumption - April 2025 (90,000 Records)

📌 Overview

This dataset presents detailed energy consumption records from various households over the month. With 90,000 rows and multiple features such as temperature, household size, air conditioning usage, and peak hour consumption, this dataset is perfect for performing time-series analysis, machine learning, and sustainability research.

📂 Dataset Summary

• Rows: 90,000
• Time Range: April 1, 2025 – April 30, 2025
• Data Granularity: Daily per household
• Location: Simulated global coverage
• Format: CSV (Comma-Separated Values)

📚 Libraries Used for Working with household_energy_consumption_2025.csv

🔍 1. Data Manipulation & Analysis

📊 2. Data Visualization

📈 3. Machine Learning / Modeling

🧹 4. Data Preprocessing

🧪 5. Model Evaluation

✅ These libraries provide a complete toolkit for performing data analysis, modeling, and visualization tasks efficiently.

📈 Potential Use Cases

This dataset is ideal for a wide variety of analytics and machine learning projects:

🔮 Forecasting & Time Series Analysis

• Predict future household energy consumption based on previous trends and weather conditions.
• Identify seasonal and daily consumption patterns.

💡 Energy Efficiency Analysis

• Analyze differences in energy consumption between households with and without air conditioning.
• Compare energy usage efficiency across varying household sizes.

🌡️ Climate Impact Studies

• Investigate how temperature affects electricity usage across households.
• Model the potential impact of climate change on residential energy demand.

🔌 Peak Load Management

• Build models to predict and manage energy demand during peak hours.
• Support research on smart grid technologies and dynamic pricing.

🧠 Machine Learning Projects

• Supervised learning (regression/classification) to predict energy consumption.
• Clustering households by usage patterns for targeted energy programs.
• Anomaly detection in energy usage for fault detection.

🛠️ Example Starter Projects

• Time-series forecasting using Facebook Prophet or ARIMA
• Regression modeling using XGBoost or LightGBM
• Classification of AC vs. non-AC household behavior
• Energy-saving recommendation systems
• Heatmaps of temperature vs. energy usage

Note: Find data at source; data is continuously updated・ PG&E provides non-confidential, aggregated usage data that are available to the public and updated on a quarterly basis. These public datasets consist of monthly consumption aggregated by ZIP code and by customer segment: Residential, Commercial, Industrial and Agricultural. The public datasets must meet the standards for aggregating and anonymizing customer data pursuant to CPUC Decision 14-05-016, as follows: a minimum of 100 Residential customers; a minimum of 15 Non-Residential customers, with no single Non-Residential customer in each sector accounting for more than 15% of the total consumption. If the aggregation standard is not met, the consumption will be combined with a neighboring ZIP code until the aggregation requirements are met.

The average energy consumption of a ChatGPT request was estimated at *** watt-hours, nearly ** times that of a regular Google search, which reportedly consumes *** Wh per request. BLOOM had a similar energy consumption, at around **** Wh per request. Meanwhile, incorporating generative AI into every Google search could lead to a power consumption of *** Wh per request, based on server power consumption estimations.

Electricity use in data centers run by Google and Microsoft accounted for ** terawatt hours in 2023, greater than that of the country of Jordan. The training of AI models has heavily contributed to an increase in energy requirements, leading a number of big tech companies to consume more energy than countries.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER. This dataset includes electricity and natural gas usage data reported at the city, town, and village level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the county and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-Community-Energy-U/4txm-py4p. Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Energy use by industries and households. Industry aggregation is at the L-level of the input-output accounts of Statistics Canada.

Primary energy consumption in North America amounted to some 116.7 exajoules in 2023, down from some 118 exajoules in the previous year. The United States' energy consumption was the highest in the region, accounting for more than 80 percent of North America's total primary energy consumption.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315, and updated the protocol in a modification order on August 12, 2021. The order requires utilities and CCA administrators under its regulation to develop and report community energy use data to the UER. This dataset includes electricity and natural gas usage data reported at the city, town, and village level. Other UER datasets include energy use data reported at the county and ZIP code level.

Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

The average for 2015 based on 34 countries was 4181.46 kilograms of oil equivalent. The highest value was in Iceland: 17478.89 kilograms of oil equivalent and the lowest value was in Mexico: 1559.12 kilograms of oil equivalent. The indicator is available from 1960 to 2015. Below is a chart for all countries where data are available.

Note: Sample data provided. ・ Displays several units of energy consumption for households, businesses, and industries in the City of Chicago during 2010. Electric The data was aggregated from ComEd and Peoples Natural Gas by Accenture. Electrical and gas usage data comprises 88 percent of Chicago's buildings in 2010. The electricity data comprises 68 percent of overall electrical usage in the city while gas data comprises 81 percent of all gas consumption in Chicago for 2010.Census blocks with less than 4 accounts is displayed at the Community Area without further geographic identifiers. This dataset also contains selected variables describing selected characteristics of the Census block population, physical housing, and occupancy.

Industrial Energy End Use in the U.S

Facility-Level Combustion Energy Data

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains in-depth facility-level information on industrial combustion energy use in the United States. It provides an essential resource for understanding consumption patterns across different sectors and industries, as reported by large emitters (>25,000 metric tons CO2e per year) under the U.S. EPA's Greenhouse Gas Reporting Program (GHGRP). Our records have been calculated using EPA default emissions factors and contain data on fuel type, location (latitude, longitude), combustion unit type and energy end use classified by manufacturing NAICS code. Additionally, our dataset reveals valuable insight into the thermal spectrum of low-temperature energy use from a 2010 Energy Information Administration Manufacturing Energy Consumption Survey (MECS). This information is critical to assessing industrial trends of energy consumption in manufacturing sectors and can serve as an informative baseline for efficient or renewable alternative plans of operation at these facilities. With this dataset you're just a few clicks away from analyzing research questions related to consumption levels across industries, waste issues associated with unconstrained fossil fuel burning practices and their environmental impacts

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides detailed information on industrial combustion energy end use in the United States. Knowing how certain industries use fuel can be valuable for those interested in reducing energy consumption and its associated environmental impacts.

• To make the most out of this dataset, users should first become familiar with what's included by looking at the columns and their respective definitions. After becoming familiar with the data, users should start to explore areas of interest such as Fuel Type, Report Year, Primary NAICS Code, Emissions Indicators etc. The more granular and specific details you can focus on will help build a stronger analysis from which to draw conclusions from your data set.

• Next steps could include filtering your data set down by region or end user type (such as direct related processes or indirect support activities). Segmenting your data set further can allow you to identify trends between fuel type used in different regions or compare emissions indicators between different processes within manufacturing industries etc. By taking a closer look through this lens you may be able to find valuable insights that can help inform better decision making when it comes to reducing energy consumption throughout industry in both public and private sectors alike.

• if exploring specific trends within industry is not something that’s of particular interest to you but rather understanding general patterns among large emitters across regions then it may be beneficial for your analysis to group like-data together and take averages over larger samples which better represent total production across an area or multiple states (timeline varies depending on needs). This approach could open up new possibilities for exploring correlations between economic productivity metrics compared against industrial energy use over periods of time which could lead towards more formal investigations about where efforts are being made towards improved resource efficiency standards among certain industries/areas of production compared against other more inefficient sectors/regionsetc — all from what's already present here!

By leveraging the information provided within this dataset users have access to many opportunities for finding all sorts of interesting yet practical insights which can have important impacts far beyond understanding just another singular statistic alone; so happy digging!

Research Ideas

• Analyzing the trends in combustion energy uses by region across different industries.
• Predicting the potential of transitioning to clean and renewable sources of energy considering the current end-uses and their magnitude based on this data.
• Creating an interactive web map application to visualize multiple industrial sites, including their energy sources and emissions data from this dataset combined with other sources (EPA’s GHGRP, MECS survey, etc)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons...

Decrease the total energy use index of state-owned facilities from 128.28 kBtu/square foot in 2012 to 109.04 kBtu/square foot by 2018.

The "Energy Consumption in New York City" dataset provides comprehensive information on the energy usage patterns and trends in City over the past five years. The dataset includes data on electricity consumption, gas consumption, and water consumption in various sectors, such as residential, commercial, and industrial.

Attributes:

2020: The year for which the data was recorded. August: The month for which the data was recorded. Sector: The sector (residential, commercial, industrial) of energy consumption.

Electricity Consumption (kWh): Total electricity consumption 200,000 kWh for the specific sector. Gas Consumption (m3): Total gas consumption 500 m3 for the residential sector. Water Consumption (m3): Total water consumption 300 m3 for both residential and commercial sector.

Data Sources:

The data has been collected from industry reports, ensuring its reliability and accuracy.

Use Cases:

This dataset is valuable for researchers, urban planners, and policymakers to analyze energy consumption patterns, identify trends, and make informed decisions regarding energy efficiency and sustainability initiatives in New York City.

Update Frequency:

The dataset is updated annually to include the latest available data.

License:

The dataset is made available under the Creative Commons Attribution License, allowing users to share and adapt the data for non-commercial purposes with appropriate attribution.

Note: Sample data provided. ・ The files below contain customer energy usage data by customer type (Residential, Commercial, Industrial and Agricultural) that has been aggregated by zip code. To further protect customer energy privacy, zip codes containing usage data that does not meet specific standards set forth in CPUC Decision 14-05-016 are combined with bordering zip codes until the standards are met.SDG&E works closely with third parties that the California Public Utilities Commission has ordered California utilities to share customer information with, or are otherwise authorized to receive lawful access to customer information. Authorized third parties may request information according to the rules set forth in CPUC Decision 14-05-016.Note that data shown within this platform is 2024 Q2 data downloaded from the link listed in reference field.

The UK's direct use of energy from fossil fuels and other sources (nuclear, net imports, renewables, biofuels and waste and reallocated use of energy by industry (SIC 2007 section - 21 categories), 1990 to 2023.

the daily electricity and water consumption data of 40 users for 100 days

The data describes an electrical energy community, containing photovoltaic (PV) production profiles and end-user consumption profiles, desegregated by individual appliances used.

A dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. The data concerns a full year.

The overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles.

This work has been published in Elsevier's Data in Brief journal:
Calvin Goncalves, Ruben Barreto, Pedro Faria, Luis Gomes, Zita Vale,
Dataset of an energy community's generation and consumption with appliance allocation,
Data in Brief, Volume 45, 2022, 108590, ISSN 2352-3409,
https://doi.org/10.1016/j.dib.2022.108590
(https://www.sciencedirect.com/science/article/pii/S2352340922007971)

Reference data used to create this dataset:

• Renewable energy production profiles: https://site.ieee.org/pes-iss/data-sets/

• End-user profiles:
  • https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households
  • https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption
  • https://site.ieee.org/pes-iss/data-sets/

Displays several units of energy consumption for households, businesses, and industries in the City of Chicago during 2010. The data was aggregated from ComEd and Peoples Natural Gas by Accenture.

Census blocks with less than 4 accounts is displayed at the Community Area without further geographic identifiers. This dataset also contains selected variables describing selected characteristics of the Census block population, physical housing, and occupancy.