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- W2783492009 abstract "The large-scale pervasion of wind generators in electric power systems has raised the requirement for fast and reliable forecasting algorithms, aimed at allowing system operators to effectively manage the intrinsic uncertainties induced by their non-programmable generation profiles. To face with the dichotomy between forecasting accuracy and efficiency, this paper explores the potential role of Principal Component Analysis in training a neural-network forecaster, by extracting actionable intelligence from large data sets of historical climatic variables. The results obtained on a real case study are presented and discussed in order to assess the benefits from the application of the proposed method." @default.
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- W2783492009 date "2017-09-01" @default.
- W2783492009 modified "2023-09-23" @default.
- W2783492009 title "The role of principal component analysis in neural-based wind power forecasting" @default.
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- W2783492009 doi "https://doi.org/10.1109/isgteurope.2017.8260125" @default.
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