Daily solar power generation professional data

What is the global power generation dataset?

The dataset includes daily and hourly power generation data from fossil fuels (coal, natural gas, and oil), nuclear, hydro, wind, solar, geothermal, biomass, and other renewables for 37 countries, which covers around 70% of the global power production and 68% of global power-related CO 2 emissions.

Is solar power generation computationally intensive?

Generation of the data is computationally intensive but this dataset enables rapid assessment of solar power generation with various weather scenarios and panel configurations. 1. Data Description This dataset contains hourly power production simulation for 2019 over the Continental US (CONUS) with a 12 km spatial resolution.

What are some open-source datasets related to solar energy?

Here are some open-source datasets related to solar energy along with their links: National Renewable Energy Laboratory (NREL) Solar Radiation Data: This dataset includes solar radiation and related climatic data for locations in the United States and its territories.

How long has data been collected for power generation and weather-related data?

Over two years (2019–2020), power generation and weather-related data were collected at 15-minute intervals. The dataset was used in the Renewable Energy Generation Forecasting Competition hosted by the Chinese State Grid in 2021. The process of data collection, data processing, and potential applications are described.

Why is accurate solar and wind generation forecasting important?

Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy.

Can on-site solar and wind generation data be used for forecasting?

Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided.