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Dataset . 2022
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Data sources: Sygma
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ZENODO
Dataset . 2024
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Duferco forecast results using Kernel Ridge

Authors: Grasso, Matteo;

Duferco forecast results using Kernel Ridge

Abstract

The data uploaded represent thirteen months of Duferco forecast (June 2021 - June 2022) using Kernel Ridge regression with hourly granularity for the Calabria wind farm. The three csv files represents the three different methods developed and tested during the project: BL: Kernel Ridge regression with gaussian kernel (Baseline). PC: The Baseline method with the pre-processing of the data using the power curve. PC+K: The Baseline method with the pre-processing of the data using the power curve, and the post-processing of the forecast using Kalman smoothing filter.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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