Massively parallel data analytics for smart grid applications

Kardoš, Juraj; Holt, Timothy; Fazio, Vincenzo; Fabietti, Luca; Spazzini, Filippo; Schenk, Olaf

doi:10.1016/j.segan.2022.100789

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Firstly, the judgement matrix is constructed, and the process of construction is as follows: according to the relevant literature and experts' opinions, the relative importance of the constructed indicator system is scored between two and two indicators, and the 1-9 scale method is introduced here [7] . The former is larger than the latter 7…”

Section: Comparison With Hierarchical Analysismentioning

confidence: 99%

Research and development of comprehensive capability assessment models for modern smart distribution grids

Wang,

Han,

Liang

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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With the accelerated process of Chinese-style modernization and the establishment of a new energy system, the requirements for the construction of a modern intelligent power distribution network are constantly improving. For the comprehensive assessment of the distribution network, the self-attention algorithm can fully take into account the correlation between various indicators in the face of the huge and complex distribution network, so as to carry out the comprehensive assessment in a more comprehensive and efficient way. Therefore, this paper proposes a fuzzy comprehensive evaluation method based on self-attention mechanism. Firstly, the indicator system is established, the attention weights of all indicators are calculated by the self-attention algorithm, and then the assessment results are derived by combining the relevant regional data through the fuzzy comprehensive evaluation method. The experimental results show that the adopted evaluation method can efficiently and comprehensively derive the evaluation results, and at the same time has good training stability and flexibility.

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Section: Comparison With Hierarchical Analysismentioning

confidence: 99%