ANALYSIS OF METHODS OF INCREASING DATA RELIABILITY FOR PROBLEMS OF SHORT TERM FORECASTING OF NODAL LOAD | Zendy

Pavlo Shymaniuk | Zendy; V. Miroshnyk | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

ANALYSIS OF METHODS OF INCREASING DATA RELIABILITY FOR PROBLEMS OF SHORT TERM FORECASTING OF NODAL LOAD

Author(s) -

Pavlo Shymaniuk,

V. Miroshnyk

Publication year - 2021

Publication title -

praci institutu elektrodinamiki nacionalanoi akademii nauk ukraini

Language(s) - English

Resource type - Journals

eISSN - 1727-9895

pISSN - 2786-7064

DOI - 10.15407/publishing2021.60.051

Subject(s) - cluster analysis , reliability (semiconductor) , term (time) , data mining , series (stratigraphy) , computer science , dbscan , false positive paradox , time series , mathematics , algorithm , artificial intelligence , cure data clustering algorithm , correlation clustering , machine learning , paleontology , power (physics) , physics , quantum mechanics , biology

A comparative analysis of clustering methods was performed to identify gaps and anomalous values in the data. Data from the northwestern region of the United States were used for evaluation. According to the analysis results, it was found that the use of the DBSCAN method leads to a much smaller number of false positives. An algorithm for two-stage data validation using clustering and time series decomposition methods is proposed. Ref.9, fig. 3, tables 3.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research