Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/14652
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dc.contributor.authorRanjan K.G.
dc.contributor.authorPrusty B.R.
dc.contributor.authorJena D.
dc.date.accessioned2021-05-05T10:11:46Z-
dc.date.available2021-05-05T10:11:46Z-
dc.date.issued2021
dc.identifier.citationElectric Power Systems Research , Vol. 191 , , p. -en_US
dc.identifier.urihttps://doi.org/10.1016/j.epsr.2020.106885
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14652-
dc.description.abstractOutlier detection and correction of time-series referred to as preprocessing, play a vital role in forecasting in power systems. Rigorous research on this topic has been made in the past few decades and is still ongoing. In this paper, a detailed survey of different preprocessing methods is made, and the existing preprocessing methods are categorized. Also, the preprocessing capability of each method is highlighted. The well-established methods of each category applicable to univariate data are critically analyzed and compared based on their preprocessing ability. The result analysis includes applying the well-established methods to volatile time-series frequently used in power system applications. PV generation, load power, and ambient temperature time-series (clean and raw) of different time-step collected from various places/weather zones are considered for index-based and graphical-based comparison among the well-established methods. The impact of change in the crucial parameter(s) values and time-resolution of the data on the methods’ performance is also elucidated in this paper. The pros and cons of methods are discussed along with the scope for improvisation. © 2020en_US
dc.titleReview of preprocessing methods for univariate volatile time-series in power system applicationsen_US
dc.typeReviewen_US
Appears in Collections:5. Miscellaneous Publications

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