Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Distributed Incremental Update Scheme for Probability Distribution of
  Wind Power Forecast Error   Due to the uncertainty of distributed wind generations (DWGs), a better
understanding of the probability distributions (PD) of their wind power
forecast errors (WPFEs) can help market participants (MPs) who own DWGs perform
better during trading. Under the premise of an accurate PD model, considering
the correlation among DWGs and absorbing the new information carried by the
latest data are two ways to maintain an accurate PD. These two ways both
require the historical and latest wind power and forecast data of all DWGs.
Each MP, however, only has access to the data of its own DWGs and may refuse to
share these data with MPs belonging to other stakeholders. Besides, because of
the endless generation of new data, the PD updating burden increases sharply.
Therefore, we use the distributed strategy to deal with the data collection
problem. In addition, we further apply the incremental learning strategy to
reduce the updating burden. Finally, we propose a distributed incremental
update scheme to make each MP continually acquire the latest conditional PD of
its DWGs' WPFE. Specifically, we first use the Gaussian-mixture-model-based
(GMM-based) joint PD to characterize the correlation among DWGs. Then, we
propose a distributed modified incremental GMM algorithm to enable MPs to
update the parameters of the joint PD in a distributed and incremental manner.
After that, we further propose a distributed derivation algorithm to make MPs
derive their conditional PD of WPFE from the joint one in a distributed way.
Combining the two original algorithms, we finally achieve the complete
distributed incremental update scheme, by which each MP can continually obtain
its latest conditional PD of its DWGs' WPFE via neighborhood communication and
local calculation with its own data. The effectiveness, correctness, and
efficiency of the proposed scheme are verified using the dataset from the NREL.

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The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat