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.

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.

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.

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]

According to a 2024 forecast, global electricity consumption of data centers was projected to grow from 330 terawatt-hours in 2022 to over one petawatt-hour in 2030. This would represent around 3.7 percent of the total electricity consumption worldwide by the end of the period under consideration. Artificial intelligence accounted for around 4.5 percent of the data centers' electricity consumption in 2023. This figure is projected to grow over the next five years.

Description: This dataset contains smart meter electricity consumption data enriched with weather conditions, historical consumption statistics, and anomaly labels for detecting unusual electricity usage patterns. It is designed for anomaly detection, predictive modeling, and energy consumption analysis using advanced machine learning techniques.

Key Features: Timestamp: 30-minute interval electricity consumption records. Electricity Consumed (kWh): Power usage per time interval. Temperature (°C): External temperature affecting consumption. Humidity (%): Air humidity levels. Wind Speed (km/h): Wind conditions influencing energy needs. Avg Past Consumption (kWh): Rolling average of past power usage. Anomaly Label: Normal or abnormal usage, detected using Isolation Forest. Use Cases: Anomaly Detection: Identify fraudulent or unusual electricity consumption. Energy Efficiency Analysis: Understand consumption trends and optimize energy use. Predictive Modeling: Train AI models for forecasting electricity demand. Smart Grid Management: Improve grid stability and power distribution. This dataset is ideal for machine learning researchers, data scientists, and energy analysts developing predictive models for real-time anomaly detection and energy optimization.

based on the ESP32 hardware.

This data set contains historical electricity sales, historical number of customers, and historical average electricity prices.

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.

Saudi Arabia Electricity: Consumption data was reported at 298,701.592 GWh in 2022. This records an increase from the previous number of 292,201.759 GWh for 2021. Saudi Arabia Electricity: Consumption data is updated yearly, averaging 219,661.644 GWh from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 298,701.592 GWh in 2022 and a record low of 114,161.021 GWh in 2000. Saudi Arabia Electricity: Consumption data remains active status in CEIC and is reported by Ministry of Energy. The data is categorized under Global Database’s Saudi Arabia – Table SA.RB008: Electricity Statistics.

CN: Electricity Consumption: Industry: Mfg: Other Mfg data was reported at 77.819 kWh bn in 2022. This records an increase from the previous number of 74.239 kWh bn for 2021. CN: Electricity Consumption: Industry: Mfg: Other Mfg data is updated yearly, averaging 48.213 kWh bn from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 77.819 kWh bn in 2022 and a record low of 41.624 kWh bn in 2013. CN: Electricity Consumption: Industry: Mfg: Other Mfg data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Energy Sector – Table CN.RBB: Electricity Consumption: by Industry.

Detailed household load and solar generation in minutely to hourly resolution. This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. Data processing is conducted in Jupyter Notebooks/Python/pandas.

Latvia Electricity Consumption data was reported at 622.000 GWh in Mar 2025. This records an increase from the previous number of 621.000 GWh for Feb 2025. Latvia Electricity Consumption data is updated monthly, averaging 595.000 GWh from Jan 2006 (Median) to Mar 2025, with 231 observations. The data reached an all-time high of 776.000 GWh in Jan 2008 and a record low of 481.000 GWh in Jun 2013. Latvia Electricity Consumption data remains active status in CEIC and is reported by Central Statistical Bureau of Latvia. The data is categorized under Global Database’s Latvia – Table LV.RB002: Electricity Statistics.

🏡 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

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

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.