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• W2040378767 abstract "The wind is an intermittent renewable energy source and the energy production forecast is a fundamental activity for many reasons (grid regulation, maintenance, etc.). In this work a hybrid method (based on weather forecast data, neural networks and computational fluid dynamics) and a pure neural network approach are compared in a complex terrain site. The post processing of real production data has been discovered to be a key activity. Treatment and filtering of data spreading out from the supervisory control and data acquisition system are fundamental both for training and testing methods reliability." @default.
• W2040378767 created "2016-06-24" @default.
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• W2040378767 date "2014-01-01" @default.
• W2040378767 modified "2023-10-16" @default.
• W2040378767 title "Wind Energy Forecast in Complex Sites with a Hybrid Neural Network and CFD based Method" @default.
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• W2040378767 doi "https://doi.org/10.1016/j.egypro.2014.01.021" @default.
• W2040378767 hasPublicationYear "2014" @default.
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