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A Multi-Head Attention Mechanism Base Multi-Dimensional Data Quality Evaluation Model

High-quality power data is the basis for reliable operation of power systems, efficient data processing, and effective mining of the potential value of power data. How to use big data, artificial intelligence and other technologies to evaluate the quality of power data is a hot research topic in the field of electric power. At present, most of the power data quality evaluation methods are simple and lack the research of general data quality evaluation model. Therefore, this paper proposes a multi-dimensional data quality evaluation model based on a multi-head attention mechanism. The model measures multiple indicators such as completeness, accuracy, smoothness, and correlation. The corresponding methods are used to quantify these indicators to form a data quality evaluation index system oriented to multi-dimensional indicators; then, an application feedback mechanism based on a multi-head attention network is used to correct the calculation weights and score outputs, so as to achieve the evaluation of power data quality. Finally, the validation analysis is carried out based on the electricity data of a region in China. The experimental results show that the proposed method can effectively evaluate the quality of electric power data.