2022
DOI: 10.3389/fenrg.2022.990989
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A new framework for short-term wind power probability forecasting considering spatial and temporal dependence of forecast errors

Abstract: Since deterministic prediction errors of wind power cannot be avoided, probabilistic prediction can adequately describe the uncertainty of wind power and, thus, provide further guidance to dispatching authorities for decision making. Current probabilistic prediction methods for wind power are still incomplete in mining its physical variation process. Therefore, this study constructs a new framework for short-term wind power probabilistic forecasting considering the spatio-temporal dependence of errors by minin… Show more

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Cited by 2 publications
(1 citation statement)
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“…However, this study only considers temporal dependency. Literature [32] developed a method that improves shortterm wind power probabilistic prediction by the combination of deep belief network (DBN), error scenario partitioning method that is used to mine spatial-temporal dependence of NWP data, and kernel density estimation. However, the proposed method is affected by the power characteristic curve's accuracy.…”
Section: Contributions Of This Workmentioning
confidence: 99%
“…However, this study only considers temporal dependency. Literature [32] developed a method that improves shortterm wind power probabilistic prediction by the combination of deep belief network (DBN), error scenario partitioning method that is used to mine spatial-temporal dependence of NWP data, and kernel density estimation. However, the proposed method is affected by the power characteristic curve's accuracy.…”
Section: Contributions Of This Workmentioning
confidence: 99%