Solar energy accounted for some 6.91 percent of electricity generation in the United States in 2024, up from a 5.62 percent share a year earlier. California was the state with the largest percentage of its electricity generation covered by solar, with approximately 28.2 percent.

The UK's energy use from renewable and waste sources, by source (for example, hydroelectric power, wind, wave, solar, and so on) and industry (SIC 2007 section - 21 categories), 1990 to 2023.

In 2024, net solar power generation in the United States reached its highest point yet at 218.5 terawatt hours of solar thermal and photovoltaic (PV) power. Solar power generation has increased drastically over the past two decades, especially since 2011, when it hovered just below two terawatt hours. The U.S. solar industry In the United States, an exceptionally high number of solar-related jobs are based in California. With a boost from state legislation, California has long been a forerunner in solar technology. In the second quarter of 2024, it had a cumulative solar PV capacity of more than 48 gigawatts. Outside of California, Texas, Florida, and North Carolina were the states with the largest solar PV capacity. Clean energy in the U.S. In recent years, solar power generation has seen more rapid growth than wind power in the United States. However, among renewables used for electricity, wind has been a more common and substantial source for the past decade. Wind power surpassed conventional hydropower as the largest source of renewable electricity in 2019. While there are major environmental costs often associated with the construction and operation of large hydropower facilities, hydro remains a vital source of electricity generation for the United States.

Since 2010, global solar energy production continuously rose to its peak, at over *** petawatt hours in 2024. This represents an increase of roughly **** percent from the previous year. Overall, figures increased by more than *** petawatt hours in the period of consideration.

Wind & Solar Energy Production Dataset contains hourly wind and solar generation data from France spanning January 2020 to November 2025, featuring 51,864 complete records with 9 key columns.

It includes temporal features (date, hours, day-of-year, day name, month, season) and source classification (Wind, Solar, Mixed), with total production ranging from 58 to 23,446 MWh per hour and wind dominating at 81.9% of records.

This comprehensive dataset supports advanced renewable energy forecasting through regression and time series models, detailed pattern analysis of diurnal/seasonal/weekly trends, machine learning applications like classification and clustering, anomaly detection for production outliers, and statistical trend evaluation.

📋 Dataset Information

🎯 Purpose & Use Cases

Primary Use Cases

• ⚡ Energy Forecasting: Predict future renewable energy production
• 📊 Pattern Analysis: Identify seasonal and temporal patterns in energy generation
• 🤖 Machine Learning: Build predictive and classification models
• 📈 Time Series Analysis: Analyze energy production trends
• 🔍 Anomaly Detection: Identify unusual production events

Suitable For

• Regression Models (Production Prediction)
• Classification Models (Source Identification)
• Time Series Forecasting
• Clustering & Pattern Discovery
• Deep Learning Applications
• Statistical Analysis

🗂️ Features & Columns

Feature Descriptions

📊 Statistical Summary

Production Statistics (MWh)

Count    51,864
Mean     6,215.07
Std Dev   3,978.36
Min     58
25%     3,111
Median    5,372
75%     8,501
Max     23,446

Energy Source Distribution

Seasonal Distribution

Temporal Coverage

🛠️ Preprocessing & Feature Engineering

Original Data Transformation

Original Dataset: 4 columns

• Date
• Heure (Time)
• prod_eolienne_MWh (Wind Production)
• prod_solaire_MWh (Solar Production)

Engineered Features: 5 new features added

1. Start_Hour & End_Hour - Temporal...

🏠 TNEB Household Power Consumption (Before Solar Installation) 🔋 Tamil Nadu Home Electricity Usage – Real Data before Rooftop Solar Setup 📌 Dataset Description This dataset contains monthly power consumption data for a residential customer under Tamil Nadu Electricity Board (TNEB) prior to the installation of a rooftop solar system.

The objective is to help analyze energy usage patterns in Indian households, estimate the potential benefits of solar adoption, and support machine learning models related to energy forecasting and savings calculation.

📂 What’s Included Column Name Description Assessment Date Date of consumption record Entry Date Date of entry into system Status Meter status – NORMAL or NOT IN USE KWH Energy consumption in kilowatt-hours Recorded Demand (kW) Maximum recorded demand Power Factor Average power factor CC Charges Consumption charges Electricity Tax Applicable tax Fixed Charges Monthly fixed charges Total Charges Total calculated charges Advance Paid Any advance amount paid Adjustment Adjustment entries Amount To Be Paid Final bill amount after deductions Due Date Last date for payment Amount Paid Paid amount Receipt No Transaction receipt number Payment Date Date of payment

🧾 File Information Filename: TNEB_Power_Consumption_Before_Solar.csv

Rows: 25+

Time Period: June 2021 – April 2025

Data Format: CSV (Comma-Separated Values)

Size: ~20 KB

🖼️ Images Include these images in your dataset (upload to Kaggle interface):

📊 Monthly Power Consumption Chart – bar graph of units consumed month-wise.

💸 Bill Amount Trend – line chart showing monthly bill values.

☀️ Before vs After Solar Comparison (optional) – if you plan to upload the after-solar dataset.

✅ Use Cases Energy load pattern analysis

Solar feasibility estimation

Consumption forecasting (ML models)

Real-world use for electrical savings simulation

📌 Future Scope A follow-up dataset for post-solar installation data can be added to help compare savings and performance improvement.

Description: This dataset contains information on renewable energy adoption and usage by households around the world, spanning the years 2020 to 2024. It provides insights into the growing trend of renewable energy sources like solar, wind, hydro, and geothermal being utilized in residential settings. The data includes factors such as household size, income levels, urban vs rural locations, and government subsidies, which may influence renewable energy adoption and consumption.

Features: Household_ID: A unique identifier for each household.

Region: The geographical region where the household is located (e.g., North America, Europe, Asia).

Country: The specific country of the household.

Energy_Source: The type of renewable energy being used by the household (e.g., Solar, Wind, Hydro).

Monthly_Usage_kWh: The monthly energy consumption in kilowatt-hours.

Year: The year the data was recorded (2020-2024).

Household_Size: The number of people living in the household.

Income_Level: The income bracket of the household (Low, Middle, High).

Urban_Rural: Whether the household is in an urban or rural area.

Adoption_Year: The year the household first adopted renewable energy.

Subsidy_Received: Whether the household received any government subsidies for renewable energy (Yes/No).

Cost_Savings_USD: The monthly savings in USD due to using renewable energy.

Usage: This dataset can be used for:

Analyzing the adoption trends of renewable energy across different countries and regions. Understanding the impact of income levels and household size on renewable energy adoption. Comparing energy usage patterns between urban and rural areas. Exploring the role of government subsidies in encouraging renewable energy usage.

This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2023, figures for 2024 are revised provisional.

Changes as of December 2025: Figures on biomass boilers for heat only have been revised, resulting in a slight increase for 2021.

Changes as of November 2025: Figures have been revised from 2021 – 2022 and updated for 2023 -2024 The revision concerns improved data on (bio)diesel oil consumption by mobile equipment in the construction and services sectors. This results in a shift of biodiesel consumption in energy application transport to energy application heating and cooling. These changes amount to a few PJ.

Changes as of june 2025: Figures for 2024 have been added.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

The data has been sourced from the International Renewable Energy Agency (https://pxweb.irena.org/pxweb/en/IRENASTAT). The indicators on energy transition have been formulated to help users understand the progress in the adoption of renewable energy sources vis-à-vis the increasing energy requirements. Sources: International Renewable Energy Agency (IRENA) (2024), Renewable Energy Statistics 2024, https://pxweb.irena.org/pxweb/en/IRENASTAT; IMF Staff Calculations. Category: Mitigation,Transition to a Low-Carbon Economy Data series: Electricity Generation Electricity Installed Capacity Metadata: Electricity generation: The gross electricity produced by electricity plants, combined heat and power plants (CHP) and the distribution generators measured at the output terminals of generation. It includes on-grid and off-grid generation, and it also includes the electricity self-consumed in energy industries; not only the electricity fed into the grid (net electricity generation). The indicator is expressed in the Dashboard in Gigawatt hours (GWh). Electricity Installed Capacity: The maximum active power that can be supplied continuously (i.e., throughout a prolonged period in a day with the whole plant running) at the point of outlet (i.e. after taking the power supplies for the station auxiliaries and allowing for the losses in those transformers considered integral to the station). This assumes no restriction of interconnection to the network. It does not include overload capacity that can only be sustained for a short period of time (e.g., internal combustion engines momentarily running above their rated capacity). For most countries and technologies, the data on installed capacity on the Dashboard reflects the capacity installed and connected at the end of the calendar year and are expressed in Mega Watts (MW). The renewable power capacity data shown in these tables represents the maximum net generating capacity of power plants and other installations that use renewable energy sources to produce electricity. For most countries and technologies, the data reflects the capacity installed and connected at the end of the calendar year. Pumped storage is included in total capacity but excluded from total generation. The capacity data are presented in megawatts (MW) and the generation data are presented in gigawatt-hours (GWh). All the data are rounded to the nearest one MW/GWh, with figures between zero and 0.5 shown as a 0.

The United States consumed over one quadrillion British thermal units of solar thermal and photovoltaic energy in 2024. This was the highest amount consumed yet and an increase of over 100 trillion British thermal units compared to the previous year.

"I was curious about how long it would take the Sun to provide the same amount of energy that the entire planet consumes in one year."

Introduction: Unveiling the Sun's Powerhouse Potential

This project delves into the immense power of the sun, Earth's ultimate energy source. We'll explore the staggering amount of solar energy our planet receives and compare it to our current global energy consumption.

The visualisations accompanying this analysis provide a clear picture of energy usage across different regions.

This project is motivated by the ever-growing importance of renewable energy. Here's a closer look at the key benefits: - Environmental Impact: Renewable energy sources like solar power significantly reduce greenhouse gas emissions and air and water pollution, contributing to a healthier planet. - Energy Security: By utilising local and sustainable resources, we can lessen dependence on imported fuels and enhance energy security. - Economic Benefits: The renewable energy sector is a significant job creator, driving innovation and economic growth.

Earth's Annual Energy Consumption

The total energy consumption of the entire world in one year, 2023. Is approximately 619.63 exajoules (EJ).

Visualisations from the data: World_Energy_By_Country_And_Region_1965_to_2023.csv

The following charts cover 7 different regions and the whole world: The Whole World, Africa, Asia Pacific, Middle East, CIS, Europe, S. & Cent. America and North America.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fd18241117a4b6c4892f67e96c71b2e12%2FScreenshot%202024-07-05%2013.53.54.png?generation=1720184141232149&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F5a692729b5c70d0e3a71d3db119b4c8d%2FScreenshot%202024-07-05%2013.52.53.png?generation=1720186159765746&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fa98e3ef98f9529cf34335c58c9f8ac9b%2FScreenshot%202024-07-05%2018.05.20.png?generation=1720199238395470&alt=media" alt=" ">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F2ebd8276ac69caae6dcd4efb2878e2dd%2FScreenshot%202024-07-05%2018.09.40.png?generation=1720199445880343&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F261aa2f483fc1ab7ab8b5f3565433785%2FScreenshot%202024-07-05%2018.12.04.png?generation=1720199584919829&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F2aab372e7f7ec8250b0a5b9fed321a43%2FScreenshot%202024-07-05%2018.13.50.png?generation=1720199684200311&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F42d6bc90db1b167c7ae1edf16785876a%2FScreenshot%202024-07-05%2018.16.02.png?generation=1720199821470803&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fae8be01e8ad66e4334fec693be6f7454%2FScreenshot%202024-07-05%2018.18.51.png?generation=1720199975498846&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fb003b13a4cd92398bd2294f3e259b472%2FScreenshot%202024-07-05%2018.20.03.png?generation=1720200083408527&alt=media" alt="">

A Markdown document with R code for the above charts: link

The following table covers the conversion from common energy units like watt-seconds and kilowatt-hours up to large-scale measurements like exajoules.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Ff5e311ce299a07e0ef3a5ea377488a9a%2FScreenshot%202024-07-05%2013.59.09.png?generation=1720184501711815&alt=media" alt=" ">

A screenshot of energy usage from Worldometer. link

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F786a21255777d667632ab77ff9f30e99%2FScreenshot%202024-07-02%2016.47.01.png?generation=1720186360698589&alt=media" alt="">

Worldometer is run by an international team of developers, researchers, and volunteers with the goal of making world statistics available in a thought-provoking and time relevant format to a wide audience around the world.

The Importance of Renewable Energy

Environmental Impact: - Reduction of Greenhouse Gases: Renewable energy sources emit little to no greenhouse gases during operation, significantly reducing the carbon footprint and mitigating climate change. - Less Pollution: These sources generate minimal air and water pollution, contributing to cleaner air and water, which is beneficial for human health and biodiversity.

Energy Security: - Sustainable Supply: Unlike fossil fuels, renewable...

The Global Renewable Energy and Indicators Dataset is a comprehensive resource designed for in-depth analysis and research in the field of renewable energy. This dataset includes detailed information on renewable energy production, socio-economic factors, and environmental indicators from around the world. Key features include:

1.Renewable Energy Data: Covers various types of renewable energy sources such as solar, wind, hydro, and geothermal energy, detailing their production (in GWh), installed capacity (in MW), and investments (in USD) across different countries and years.

2.Socio-Economic Indicators: Includes data on population, GDP, energy consumption, energy exports and imports, CO2 emissions, renewable energy jobs, government policies, R&D expenditure, and renewable energy targets.

3.Environmental Factors: Provides information on average annual temperature, annual rainfall, solar irradiance, wind speed, hydro potential, geothermal potential, and biomass availability.

4.Additional Features: Contains relevant features such as energy storage capacity, grid integration capability, electricity prices, energy subsidies, international aid for renewables, public awareness scores, energy efficiency programs, urbanization rate, industrialization rate, energy market liberalization, renewable energy patents, educational level, technology transfer agreements, renewable energy education programs, local manufacturing capacity, import tariffs, export incentives, natural disasters, political stability, corruption perception index, regulatory quality, rule of law, control of corruption, economic freedom index, ease of doing business, innovation index, number of research institutions, renewable energy conferences, renewable energy publications, energy sector workforce, proportion of energy from renewables, public-private partnerships, and regional renewable energy cooperation.

This dataset is ideal for analysts, researchers, and policymakers aiming to study trends, impacts, and strategies related to renewable energy development globally.

CN: Electricity Production: Solar: YoY: Anhui data was reported at 3.600 % in Dec 2025. This records a decrease from the previous number of 17.000 % for Nov 2025. CN: Electricity Production: Solar: YoY: Anhui data is updated monthly, averaging -6.100 % from May 2016 (Median) to Dec 2025, with 98 observations. The data reached an all-time high of 346.700 % in Nov 2016 and a record low of -29.600 % in Jul 2020. CN: Electricity Production: Solar: YoY: Anhui 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.RBA: Energy Production: Electricity: Solar.

EMHIRES Wind

The first version of EMHIRES dataset releases four different files about the wind power generation hourly time series during 30 years (1986-2015), taking into account the existing wind fleet at the end of 2015, for each country (onshore and offshore), bidding zone and by NUTS 1 and NUTS 2 region. The time series are given as capacity factors. The installed capacity used accounted for calculating the capacity factors are summarised in the annexes of the report.

• https://setis.ec.europa.eu/emhires-dataset-part-i-wind-power-generation_en

EMHIRES Solar

EMHIRES provides RES-E generation time series for the EU-28 and neighbouring countries. The solar power time series are released at hourly granularity and at different aggregation levels: by country, power market bidding zone, and by the European Nomenclature of territorial units for statistics (NUTS) defined by EUROSTAT; in particular, by NUTS 1 and NUTS 2 level. The time series provided by bidding zones include special aggregations to reflect the power market reality where this deviates from political or territorial boundaries.

The overall scope of EMHIRES is to allow users to assess the impact of meteorological and climate variability on the generation of solar power in Europe and not to mime the actual evolution of solar power production in the latest decades. For this reason, the hourly solar power generation time series are released for meteorological conditions of the years 1986-2015 (30 years) without considering any changes in the solar installed capacity. Thus, the installed capacity considered is fixed as the one installed at the end of 2015. For this reason, data from EMHIRES should not be compared with actual power generation data other than referring to the reference year 2015.

• https://setis.ec.europa.eu/emhires-dataset-part-ii-solar-power-generation_en

Is supplemented by Journal article: https://www.sciencedirect.com/science/article/pii/S0306261917312515?via%3Dihub Journal article: https://www.sciencedirect.com/science/article/pii/S0306261917304622?via%3Dihub

Citation Gonzalez-Aparicio, I., Zucker, A., Careri, F., Monforti, F., Huld, T., & Badger, J. (2021). EMHIRES dataset: wind and solar power generation [archived] [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4803353

Data are taken from the Microgeneration Certification Scheme - MCS Installation Database.

For enquiries concerning this table email fitstatistics@energysecurity.gov.uk.

The statistics Solar Energy, sub-statistic of the Swiss Renewable Energy Statistics, provides information on the installed area/performance and production of solar systems (heat, electricity) in Switzerland on an annual basis. Solar energy statistics are part of Switzerland’s public statistics (legal basis: BStatG).

CN: Electricity Production: Solar: YoY: Fujian data was reported at 43.200 % in Dec 2025. This records a decrease from the previous number of 71.200 % for Nov 2025. CN: Electricity Production: Solar: YoY: Fujian data is updated monthly, averaging -11.500 % from May 2016 (Median) to Dec 2025, with 98 observations. The data reached an all-time high of 434.800 % in May 2018 and a record low of -80.700 % in May 2017. CN: Electricity Production: Solar: YoY: Fujian 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.RBA: Energy Production: Electricity: Solar.

This data has been gathered from the city of Calgary's solar photovoltaic projects.

Solar power generation data gathered (in MW) on an hourly basis for the Calgary's solar photovoltaic projects, from 2017 September to 2024 February.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8127972%2Fa68dd79afea8293b94137ba7cf281f5b%2Fsolar-pv-farm-turkey-1024x587.png?generation=1709721545961616&alt=media" alt="">

The dataset offers valuable opportunities for various machine learning applications in the domain of renewable energy and power market analysis. Here are some potential use cases:

1. Time Series Forecasting: Machine learning models can be trained on the hourly production records to forecast future wind and solar energy production levels. These predictions are crucial for grid operators, energy traders, and policymakers to plan and optimize energy distribution and utilization efficiently.

2. Anomaly Detection: By employing machine learning algorithms, anomalies in energy production patterns can be detected. Anomalies may indicate equipment malfunctions, weather-related issues, or other irregularities that require attention.

3. Predict the power generation for next couple of days, for the better grid management.

Acknowledgment: https://www.calgary.ca/environment/programs/solar.html?redirect=/solar

CN: Electricity Production: Solar Photovoltaic data was reported at 839.040 kWh bn in 2024. This records an increase from the previous number of 584.264 kWh bn for 2023. CN: Electricity Production: Solar Photovoltaic data is updated yearly, averaging 176.900 kWh bn from Dec 2013 (Median) to 2024, with 12 observations. The data reached an all-time high of 839.040 kWh bn in 2024 and a record low of 8.374 kWh bn in 2013. CN: Electricity Production: Solar Photovoltaic 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.RBA: Energy Production: Electricity: Solar.