Dataset used in the article entitled 'Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools'. These datasets can be used to test several characteristics in machine learning and data processing algorithms.

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Generator Market In Data Centers Size 2024-2028

The generator market in data centers size is forecast to increase by USD 4.26 billion at a CAGR of 8.56% between 2023 and 2028. In the realm of data center operations, power reliability emerges as a critical factor, driving the market's growth. Next-generation power monitoring and management software are increasingly being adopted to ensure uninterrupted power supply and enhance overall efficiency. However, the data center industry's carbon footprint is a significant concern, leading to the exploration of renewable energy sources such as wind, solar, and hydroelectric power. Micro-economic factors, including the rising cost of fossil fuels and the growing popularity of nuclear energy, are also influencing market dynamics. Edge computing sites are gaining traction, necessitating the need for power solutions that cater to their unique requirements.

What will be the Size of the Market During the Forecast Period?

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The market play a pivotal role in the digital transformation of businesses, enabling the storage, processing, and dissemination of critical information. However, power interruptions and system downtime can lead to significant information loss and revenue damage. To mitigate these risks, data center operators are increasingly investing in power backup solutions. Power density, the amount of power used per unit area, is a critical factor in data center design. Edge data centers, which are smaller and closer to the source of data generation, require innovative power backup solutions due to their limited space.

Moreover, 5G technology and edge computing are driving the growth of edge data centers, necessitating the development of compact, efficient power backup systems. Power costs are a significant expense for data center operators. Fuel cells, solar-powered data parks, natural gas generators, and diesel generators are among the power backup solutions that offer cost-effective alternatives to traditional grid power. Li-ion batteries are gaining popularity as they provide high energy density and long cycle life. Colocation service providers offer customized capacity solutions to meet the unique power requirements of their clients. Power backup solutions, including backup power systems and power loss prediction technologies, are essential components of their offerings.

Furthermore, these solutions ensure uninterrupted power supply and enhance data center reliability. Electricity is the primary power source for data centers. Power backup solutions provide a safety net against power interruptions, ensuring business continuity. Power loss prediction technologies enable data center operators to anticipate power outages and take preventive measures. The generator market is witnessing significant growth due to the increasing demand for power backup solutions. Fuel cells, solar-powered data parks, natural gas generators, and diesel generators are among the generator types that cater to the power backup needs of data centers. In conclusion, power backup solutions are a critical component of data center infrastructure.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Diesel
  Gas


Capacity

  Less than 1MW
  1MW-2MW
  More than 2MW


Geography

  North America

    US


  Europe

    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Type Insights

The diesel segment is estimated to witness significant growth during the forecast period.In the data center industry, diesel generators play a significant role in providing power during fluctuating or transient scenarios. Their high-torque performance characteristic makes them an ideal choice for data centers with high power density requirements. Diesel generators come in various capacity ranges, making them a versatile option for data centers of all sizes. The diesel generator system consists of several components, including the diesel engine, generating unit, fuel storage/supply, and electrical switchgear. These generators are popular due to their reliability, safety, and minimal maintenance requirements. The output power capacity of diesel generators is greater than other types, making them suitable for large data center infrastructure.

Furthermore, diesel fuel is the most commonly used fuel in generators installed in data centers. The cost-effectiveness of diesel generators is another reason for their popularity. However, electricity prices and taxes can impact the overall cost of operating a data center with diesel generators. Edge data centers and colocation service providers are increasingly adopting 5G technology, which may require even more power density

Dataset Card for test-data-generator

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/franciscoflorencio/test-data-generator/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/franciscoflorencio/test-data-generator.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

This dataset compiles estimated generator unavailability for eight countries in Northwest Europe, plus Spain. The advantages and limitations of the data are described in detail in the paper submitted to the PMAPS 2022 (Manchester) conference, “Comparing Generator Unavailability Models with Empirical Distributions from Open Energy Datasets” (submitted); the code used to generate the csvs in this dataset are provided at https://github.com/deakinmt/entsoe_outage_models

The dataset consists of forced, planned and total outages, calculated by aggregating the unavailabilities reported in an individual balancing zone. An estimate of the uncertainty due to apparent inconsistencies in outage reports is also provided (also described in the paper).

The global synthetic data market size is projected to grow from USD 0.4 billion in the current year to USD 19.22 billion by 2035, representing a CAGR of 42.14%, during the forecast period till 2035

The Form EIA-860 is a generator-level survey that collects specific information about existing and planned generators and associated environmental equipment at electric power plants with 1 megawatt or greater of combined nameplate capacity. The survey data is summarized in reports such as the Electric Power Annual. The survey data is also available for download here.

The data are compressed into a self-extracting (.exe) zip folder containing .XLS data files and record layouts. The current file structure (starting with 2009 data) consists of a record layout, 8 data files and a copy of the applicable version of the Form EIA-860 on which the data was collected.

The record layout provides a directory of all (published) data elements collected on the Form EIA-860 together with the related description, specific file location(s), and, where appropriate, explanation of codes. The data files consist of the following (substitute the applicable year for "yy" in the file name):

UtylityY*yy* - Contains respondent contact information and utility-level data for the surveyed generators.

PlantY*yy* - Contains plant-level data for the surveyed generators.

GeneratorY*yy* - Contains generator-level data for the surveyed generators, split into three tabs. The "Exist" tab includes those generators which are currently operating, out of service or on standby; the "Prop" tab includes those generators which are planned and not yet in operation; and the "Ret_IP" tab includes those generators which were cancelled prior to completion and operation and retired generators at existing plants (does not include data for retired generators at plants at which all generators have been retired).

OwnerY*yy* - Contains data on the owner and/or operator of the surveyed generators.

MultiFuelY*yy* - Contains data on fuel-switching and the use of multiple fuels by surveyed generators, split into three tabs: "Exist," "Prop," and "Ret_IP." See GeneratorsYyy above for a description of the tabs.

InterconnectY*yy* - Contains interconnection data for the surveyed generators.

EnviroAssocY*yy* - Contains boiler association data for the environmental equipment data collected on the Form EIA-860. The "Boiler_Gen" identifies which boilers are associated with each generator; the "Boiler_Cool" tab shows which cooling systems are associated with each boiler; the "Boiler_FGD" tab shows which flue gas desulfurization (FGD) systems are associated with each boiler; the "Boiler_FGP" tab shows which flue gas particulate (FGP) collectors are associated with each boiler; and the "Boiler_SF" tab shows which stacks and flues are associated with each boiler.

EnviroEquipY*yy* - Contains environmental equipment data for the surveyed generators. The "Boiler" tab collects boiler data as collected on Schedule 6, Parts B, and C of the Form EIA-860; "Control" tab contains emission data as collected on Schedule 6, Parts D and E; the "Cooling" tab collects cooling system data as collected on Schedule 6, Part F; the "FGD" tab collects FGD data as collected on Schedule 6, Part H; the "FGP" tab collects FGP data as collected on Schedule 6, Part G; and the "StackFlue" tab collects stack and flue data as collected on Schedule 6, Part I.

EIA Contact: Vlad Dorjets, phone: 202-586-3141, e-mail: Vlad Dorjets

The HazWaste database contains generator (companies and/or individuals) site and mailing address information, waste generation, the amount of waste generated etc. of all the hazardous waste generators in Vermont. Database was developed in early 1990's for program management and to meet EPA Authorization requirements. The database has been updated to more modern data systems periodically.�

The Data Center Backup Generator Market Report is Segmented by Product Type (Diesel, Natural Gas, and Other Product Types), Capacity (Less Than 1MW, 1-2MW, Greater Than 2MW), Tier (Tier I and II, Tier III, Tier IV), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The synthetic data generation market is projected to be worth US$ 300 million in 2024. The market is anticipated to reach US$ 13.0 billion by 2034. The market is further expected to surge at a CAGR of 45.9% during the forecast period 2024 to 2034.

Country-wise Insights

Category-wise Insights

Report Scope

The Synthetic Data Generation Market size is expected to reach a valuation of USD 36.09 Billion in 2033 growing at a CAGR of 39.45%. The research report classifies market by share, trend, demand and based on segmentation by Data Type, Modeling Type, Offering, Application, End Use and Regional Outlook.

These are cleaned, compiled, and geocoded datasets of publicly available EIA-860 data for 2001-2018. Original data is found here: https://www.eia.gov/electricity/data/eia860/

The Data Center Generator market is experiencing robust growth, projected to reach a market size of $7,083 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 4.4% from 2025 to 2033. This expansion is driven by several key factors. The increasing demand for reliable power backup in data centers to mitigate the risk of data loss and downtime due to power outages is a primary driver. The rising adoption of cloud computing and the proliferation of edge data centers are further fueling market growth. Furthermore, advancements in generator technology, leading to improved efficiency, reduced emissions, and enhanced operational reliability, are contributing to market expansion. Stringent government regulations concerning data security and operational continuity are also pushing data center operators to invest heavily in robust backup power solutions. The market is segmented by generator type (1MW-2MW and >2MW) and application (Diesel Generators, DRUPS Systems, and Others), allowing for targeted investment and development strategies. Leading players like Caterpillar, Cummins, and Generac are capitalizing on these trends through continuous innovation and strategic partnerships. Competition is fierce, with established players and new entrants vying for market share through technological advancements and competitive pricing. The geographical distribution of the market is diverse, with North America, Europe, and Asia Pacific representing significant market segments. However, emerging economies in Asia Pacific and the Middle East & Africa are witnessing rapid growth, presenting lucrative opportunities for market expansion. While the initial investment costs associated with data center generators can be high, the long-term benefits in terms of data security, operational resilience, and business continuity significantly outweigh the initial expense. The increasing adoption of smart grid technologies and microgrids is further shaping the market landscape, enhancing the integration of data center generators into broader power management systems. The forecast period reflects continued growth based on these ongoing trends and the anticipated growth of the data center industry itself. This report provides a comprehensive analysis of the Data Center Generator market, projected to reach $15 billion by 2030. It delves into key trends, regional dynamics, and competitive landscapes, offering invaluable insights for stakeholders across the industry.

China Generator & Generator Set: Sales Revenue: Year to Date data was reported at 337.522 RMB bn in Oct 2015. This records an increase from the previous number of 297.439 RMB bn for Sep 2015. China Generator & Generator Set: Sales Revenue: Year to Date data is updated monthly, averaging 82.796 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 382.490 RMB bn in Dec 2014 and a record low of 4.744 RMB bn in Feb 2006. China Generator & Generator Set: Sales Revenue: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIA: Motor: Generator and Generator Set.

Analyze the market segmentation of the Synthetic Data Generation (SDG) industry. Gain insights into market share distribution with a detailed breakdown of key segments and their growth.

Data Center Generator Market is estimated to reach USD 15.3 billion By 2033, Riding on a Strong 7% CAGR throughout the forecast period.

The synthetic data generation market size is projected to grow from USD 307.42 million to USD 18.23 billion, witnessing a CAGR of over 36.9% during the forecast period, between 2025 and 2037. North America region is attributed to hold the largest revenue share of about 33% by 2037 due to the increasing technological advancements in the region.

142 Global import shipment records of Generators with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The table 6_1_EnviroAssoc_Y2021_Early_Release_Boiler Generator is part of the dataset EIA 860 (Annual Electric Generator Data), 2021, available at https://redivis.com/datasets/axt6-57e1ch05p. It contains 7260 rows across 7 variables.

Synthetic Data Generation Market Size

As per the latest insights from Market.us, the Global Synthetic Data Generation Market is set to reach USD 6,637.98 million by 2034, expanding at a CAGR of 35.7% from 2025 to 2034. The market, valued at USD 313.50 million in 2024, is witnessing rapid growth due to rising demand for high-quality, privacy-compliant, and AI-driven data solutions.

North America dominated in 2024, securing over 35% of the market, with revenues surpassing USD 109.7 million. The region’s leadership is fueled by strong investments in artificial intelligence, machine learning, and data security across industries such as healthcare, finance, and autonomous systems. With increasing reliance on synthetic data to enhance AI model training and reduce data privacy risks, the market is poised for significant expansion in the coming years.