Renewable energy sources exhibit intermittency due to the geophysical drivers of the resource. Understanding the magnitudes and time scales of intermittency aids the planning of grid
This paper describes relevant issues of the energy prediction from onshore wind farms. The use of a neural network to forecast wind power
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The repository contains wind speeds and generation based on three different meteorological models: ERA5, MERRA2, and HRRR. Data are publicly accessible in simple csv files.
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However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind
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Wind power output changes intermittently depending on weather conditions. In electrical power systems with high shares of variable renewable energy sources, such as wind power, system
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Future energy systems will rely on renewable power sources, especially wind and solar power. Their operation depends on the weather and is thus highly variable and uncertain.
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Moving beyond the one-month-ahead horizon, monthly forecasts project average wind-speed or -power generation for each month over a multi
Wind power generation fluctuates because of continually changing wind speeds. Accurate forecasting models are required for successfully integrating such fluctuating generation into the grid and market.
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In a first part, we reconstruct surface wind distributions on monthly and seasonal timescales from the knowledge of the large-scale state of the atmosphere, which is summarized
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