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• W2897941349 abstract "The better prediction models for the upcoming supply of renewable energy are important to decrease the need for controlling energy provided by conventional power plants. Especially for successful power grid integration of the highly volatile wind power production, a reliable forecast is crucial. In this chapter, we focus on short-term wind power prediction and employ data from the National Renewable Energy Laboratory (NREL), which are designed for a wind integration study in the western part of the United States. In contrast to physical approaches based on very complex differential equations, our model derives functional dependencies directly from the observations. Hereby, we formulate the prediction task as regression problem and test different regression techniques such as linear regression and support vector regression. In our experiments, we analyze predictions for individual turbines as well as entire wind parks and show that a machine learning approach yields feasible results for short-term wind power prediction." @default.
• W2897941349 created "2018-10-26" @default.
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• W2897941349 date "2017-12-01" @default.
• W2897941349 modified "2023-09-24" @default.
• W2897941349 title "Prediction of Wind Speed Using Real Data: An Analysis of Statistical Machine Learning Techniques" @default.
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• W2897941349 doi "https://doi.org/10.1109/apwconcse.2017.00051" @default.
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