Solar energy generation is one of fastest growing and most promising renewable energy sources of power generation worldwide. Nowadays, the electrical energy becomes one of the basic needs in our daily life, which makes increasing demand for it. As a major source of electrical power generation fossil fuels are depleting day by day and also its usage raises serious environmental concerns. These reasons force the development of new energy sources which are renewable and ecologically safe. There are several applications that use solar power, here is the information on the generation of electricity through PV cells. The solar power generation is the most efficient route for power generation because it takes a minimum number of steps (for producing electricity) than that of other generation methods. PV solar cells are interconnected to form a PV module to capture the sun rays and convert solar energy into electricity. So when the PV modules are exposed to sunlight, they generate direct current. It is one of the best ways in nowadays which can transfer the solar energy into utilization. Many countries in the world have taken this technique into utilization; however, the estimation of the PV generation is a challenge because the PV system generation is greatly affected by the weather conditions.

As the weather has great influence on the production of PV systems, such as irradiation, temperature, humidity, wind velocity. The goal of this competition is to build the relationship between the weather with the production of PV systems by analyzing history data. Through the model, we are able to use predicted data of weather in the near future to predict the production of PV systems. Once the result deviate much from prediction, probably there are problems with PV system, and the reason need to be figured out, and then adopt proper measure to fix the PV system and make better decisions. For example, according to the accurate forecast, PV system operators can balance the consumption of power and reserve spared power for emergency.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

The database contains data on net electricity production at the threshold of power plants by activity (main activity, autoproducers, other producers) by type of source (hydro, thermal, nuclear, solar, wind). The data is shown on an annual basis. Unit: It’s GWh.

The dataset includes information on distributed electric generation (grid-connected) by the number of generators, number of generators less than one megawatt, total aggregate capacity, aggregate capacity used only for backup, and capacity by technology type.

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).

United States Electricity Generation data was reported at 10.729 kWh/Day bn in Mar 2025. This records a decrease from the previous number of 12.267 kWh/Day bn for Feb 2025. United States Electricity Generation data is updated monthly, averaging 10.486 kWh/Day bn from Jan 1991 (Median) to Mar 2025, with 411 observations. The data reached an all-time high of 13.886 kWh/Day bn in Jul 2024 and a record low of 7.593 kWh/Day bn in Apr 1991. United States Electricity Generation 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.

The information is used to help Transport and Main Roads in assessing the impact of development applications on the local road network and as an input into small scale traffic models.

Note: The data resources below have been updated as of 30/06/2021 and now contain 2021 data as well as additional historical data.

The South Australian Diesel Generation Database (Database) contains information on the ownership, use, location and configuration of electricity producing, diesel-fuelled generation plants in South Australia. The database was compiled at the... The South Australian Diesel Generation Database (Database) contains information on the ownership, use, location and configuration of electricity producing, diesel-fuelled generation plants in South Australia. The database was compiled at the request of RenewablesSA with the intention of increasing the available information on diesel generation use in South Australia. A voluntary survey was carried out to obtain the information. Significant information was provided by the Government of South Australia through the Department of Planning Transport and Infrastructure through the Buildings Management section and through the former Department of Minerals, Industry, Trade, Resources and Energy (now the Department for Energy and Mining). The data was updated in 2020 to reflect the current status as known by the Department for Energy and Mining. The fields were adjusted to be consistent with related power generation datasets.

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.

Through two decades of campaigns delivery and optimization, Expandi has created the most comprehensive GDPR-compliant European database covering SMB, Midmarket, and Enterprise companies. Our data base is enriched with up-to-date technographic, financial and intent data. All our data is updated regularly and includes only active companies, allowing you to reach the most relevant and appropriate customers for your business.

Our available data: • Updated company Firmographic, Financial data (revenues, financial strength, profit/loss), Decision Making Unit structure, and Key decision maker contacts (name, job title, LinkedIn profile). • Multi-language buyer intent data coming from omni-channel interactions and scored by brand and solutions. • Technographic and brand preference data. • Company IP addresses and Device ID mapping and tracking to help you identify unknown online traffic and boost the results of your awareness and branding campaigns.

Target selection criteria: • Region / State-Province • Range employees (starting from 50+) • Range Revenues • Industry / Sub-industry • Financial strength • Decision Making Unit • Technographic data • Intent data solution / Intent data stage

Data delivery options: • One-off purchase • Yearly subscription to the Expandi Data as a Service platform

Exclusion and inclusion lists are accepted for one-off purchases only.

Let’s start today to boost your demand generation campaigns and raise awareness of your brand and solutions!

The South Australian Diesel Generation Database (Database) contains publicly available information on the ownership, use, location and configuration of electricity producing, diesel-fueled …Show full descriptionThe South Australian Diesel Generation Database (Database) contains publicly available information on the ownership, use, location and configuration of electricity producing, diesel-fueled generation plants in South Australia. The database was compiled at the request of RenewablesSA with the intention of increasing the available information on diesel generation use in South Australia. A voluntary survey was carried out to obtain the information. Significant information was provided by the Government of South Australia through the Department of Planning Transport and Infrastructure through the Buildings Management section and through the former Department of Minerals, Industry, Trade, Resources and Energy (now the Department for Energy and Mining). The data set was updated in 2018 to include large and small diesel generation and includes projects under development and hybrid installations where there is more than one source of generation. Updated data has been sourced from the Department for Energy and Mining. The fields have been updated to be consistent with the SA Power Generation data set.

The dataset contains information on utility or customer-owned dispersed generation (NOT grid-connected) such as the number, capacity and types of generators.

This is a database containing techno-economic data for heat & power technologies, primarily focusing on technologies for heat generation in industry or district heating. The database is a compilation of information from literature, specifically tailored for use in TIMES models. Even though this specific database has been developed for TIMES-Sweden, the data can also be applied for other regions. The database is continuously updated as work progresses with the TIMES-Sweden model.

Preferably to be used in combination with TIMES-Sweden Industry database (10.5281/zenodo.4139800), and TIMES-Sweden Fuel production technologies database (10.5281/zenodo.6372926).

This Database is also a part of the IEA ETSAP SubRES project, with the aim to make techno-economic data more accessible. More information about ETSAP can be found here: https://iea-etsap.org/

More information about TIMES-Sweden and the modelling team can be found here: http://www.ltu.se/TIMES-Sweden

Dataset

This dataset was created by Afroz

Contents

Mexico Electric Power Generation: Net data was reported at 13,353.223 GWh in Mar 2018. This records an increase from the previous number of 11,423.278 GWh for Feb 2018. Mexico Electric Power Generation: Net data is updated monthly, averaging 11,928.000 GWh from Jan 1982 (Median) to Mar 2018, with 435 observations. The data reached an all-time high of 16,877.000 GWh in Aug 2001 and a record low of 5,396.000 GWh in Feb 1983. Mexico Electric Power Generation: Net data remains active status in CEIC and is reported by Federal Commission of Electricity. The data is categorized under Global Database’s Mexico – Table MX.RB002: Electric Power Generation: Federal Commission of Electricity (Discontinued).

The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data on characteristics of almost all electric power generated in the United States. This data includes capacity; heat input; net generation; associated air emissions of nitrogen oxides, sulfur dioxide, carbon dioxide, methane, nitrous oxide and mercury; emissions rates; resource mix (i.e., generation by fuel type); nonbaseload calculations; line losses (a.k.a., grid gross loss); and many other attributes. The data is provided at the unit and generator levels, as well as, aggregated to the plant, state, balancing authority, eGRID subregion, NERC region, and US levels. As of January 2025, the available editions of eGRID contain data for years 2023, 2022, 2021, 2020, 2019, 2018, 2016, 2014, 2012, 2010, 2009, 2007, 2005, 2004, and 1996 through 2000.

SQL Generation AI Market Outlook

According to our latest research, the global SQL Generation AI market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by the rapid adoption of artificial intelligence technologies in database management and analytics. The market is set to grow at a compelling CAGR of 27.6% from 2025 to 2033, with the total market size forecasted to reach USD 13.18 billion by 2033. This remarkable growth trajectory is primarily fueled by advancements in natural language processing, the increasing complexity of enterprise data environments, and the demand for automation in SQL query generation to enhance productivity and reduce operational costs.

The primary growth factors propelling the SQL Generation AI market revolve around the escalating need for data-driven decision-making and the democratization of data access across organizations. As enterprises generate and store vast amounts of data, the ability to quickly and accurately extract actionable insights becomes critical. SQL Generation AI solutions, leveraging advanced machine learning and natural language processing algorithms, enable non-technical users to generate complex SQL queries using simple natural language instructions. This not only reduces the dependency on specialized database administrators but also accelerates the pace of business intelligence and analytics initiatives. The proliferation of self-service analytics and the integration of AI-powered query generation into popular business intelligence platforms further amplify market growth, making it easier for organizations to unlock the value of their data assets.

Another significant driver is the ongoing digital transformation across various industries, which has led to the modernization of legacy IT infrastructures and the adoption of cloud-based data management solutions. Organizations are increasingly migrating their databases to the cloud to benefit from scalability, flexibility, and cost-efficiency. SQL Generation AI tools are being integrated with cloud data warehouses and analytics platforms, allowing for seamless query generation and real-time data analysis. This shift not only optimizes data workflows but also supports hybrid and multi-cloud strategies, enabling enterprises to manage and analyze data across diverse environments. The rising volume and diversity of data, coupled with the need for real-time insights, are compelling organizations to invest in AI-powered SQL generation to maintain a competitive edge.

Additionally, the COVID-19 pandemic has accelerated the adoption of digital technologies, including AI-driven SQL generation, as organizations seek to automate routine tasks and enhance operational resilience. The growing emphasis on remote work and distributed teams has highlighted the importance of intuitive data access and collaboration tools. SQL Generation AI solutions facilitate seamless collaboration between business users and data teams, bridging the gap between technical and non-technical stakeholders. This has led to increased demand across sectors such as BFSI, healthcare, retail, and manufacturing, where timely data insights are crucial for strategic decision-making. The market is also witnessing heightened interest from small and medium enterprises, which are leveraging AI-powered SQL generation to level the playing field with larger competitors.

Regionally, North America continues to dominate the SQL Generation AI market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of major technology vendors, early adoption of AI and cloud technologies, and a strong focus on data-driven innovation contribute to North America's leadership position. Europe is witnessing rapid growth, driven by stringent data regulations and increasing investments in digital transformation initiatives. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by expanding IT infrastructure, a burgeoning startup ecosystem, and rising demand for advanced analytics solutions in countries such as China, India, and Japan. Latin America and the Middle East & Africa are also showing promising growth potential as organizations in these regions accelerate their digital journeys.

Component Analysis

The SQL Generation AI market by component is broadly segmented into Software and Services. The software segment commands the majority market share, as organizations increasingly dep

Open sourced agent files needed to run version 1.0.0 of the dGen model. Contains all national, ISO, and state level residential and commercial agents.

enabling compact yet information-dense time-series analysis.​