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Numrering	Referens	Omslagsbild	Hitta
1.	Duvnjak Zarkovic, Sanja, 1989-, et al. (författare) Addressing Data Deficiencies in Outage Reports : A Qualitative and Machine Learning Approach 2024 Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract This study investigates outage statistics in the Swedish power system. More specifically, this paper delves into the critical analysis and enhancement of data quality, focusing on inconsistencies and missing values, i.e. unknown outage causes and unidentified faulty equipment. By carefully examining the data, noticeable gaps and deficiencies are revealed. Thus, a format for improving outage reporting using a database with 3 relations (outage summary, outage breakdown and customer breakdown) is proposed. In addition to a qualitative analysis of the data, various machine learning algorithms are explored and tested for their capability to predict the unknown values within the dataset, thereby offering a twofold solution: enhancing the accuracy of outage data and facilitating deeper, more accurate analytical capabilities. The findings and proposals within this work not only illuminate the current challenges within outage data management but also pave the way for more robust, data-driven decision-making in outage management and policy formation.

Duvnjak Zarkovic, Sanja, 1989-, et al. (författare)
Addressing Data Deficiencies in Outage Reports : A Qualitative and Machine Learning Approach
2024
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This study investigates outage statistics in the Swedish power system. More specifically, this paper delves into the critical analysis and enhancement of data quality, focusing on inconsistencies and missing values, i.e. unknown outage causes and unidentified faulty equipment. By carefully examining the data, noticeable gaps and deficiencies are revealed. Thus, a format for improving outage reporting using a database with 3 relations (outage summary, outage breakdown and customer breakdown) is proposed. In addition to a qualitative analysis of the data, various machine learning algorithms are explored and tested for their capability to predict the unknown values within the dataset, thereby offering a twofold solution: enhancing the accuracy of outage data and facilitating deeper, more accurate analytical capabilities. The findings and proposals within this work not only illuminate the current challenges within outage data management but also pave the way for more robust, data-driven decision-making in outage management and policy formation.

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