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Wind power forecasting errors modelling approach considering temporal and spatial dependence
The uncertainty of wind power forecasting significantly influences power systems with high percentage of wind power generation. Despite the wind power forecasting error causation, the temporal and spatial dependence of prediction errors has done great influence in specific applications, such as multistage scheduling and aggregated wind power integration. In this paper, Pair-Copula theory has been introduced to construct a multi-variate model which can fully considers the margin distribution and stochastic dependence characteristics of wind power forecasting errors. The characteristics of temporal and spatial dependence have been modelled, and their influences on wind power integrations have been analyzed. Model comparisons indicate that the proposed model can reveal the essential relationships of wind power forecasting uncertainty, and describe the various dependences more accurately.
Wind power forecasting errors modelling approach considering temporal and spatial dependence
The uncertainty of wind power forecasting significantly influences power systems with high percentage of wind power generation. Despite the wind power forecasting error causation, the temporal and spatial dependence of prediction errors has done great influence in specific applications, such as multistage scheduling and aggregated wind power integration. In this paper, Pair-Copula theory has been introduced to construct a multi-variate model which can fully considers the margin distribution and stochastic dependence characteristics of wind power forecasting errors. The characteristics of temporal and spatial dependence have been modelled, and their influences on wind power integrations have been analyzed. Model comparisons indicate that the proposed model can reveal the essential relationships of wind power forecasting uncertainty, and describe the various dependences more accurately.
Wind power forecasting errors modelling approach considering temporal and spatial dependence
Wei Hu (author) / Yong Min (author) / Yifan Zhou (author) / Qiuyu Lu (author)
2017
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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