To improve the objectivity and accuracy of energy efficiency evaluation of power users and meet the needs of userss demand for timely feedback and adjustment of energy efficiency

a real-time dynamic energy efficiency evaluation method for power users under edge architecture is proposed. On the basis of constructing the edge side evaluation framework

the dynamic logical relationship between the indicators is firstly analyzed based on the “Pressure-State-Response” conceptual model. The dynamic evaluation indicators are selected from multiple dimensions to construct the user energy efficiency evaluation index set. Considering the limited storage resources of edge nodes

the three attributes of importance

balance and independence of indicators are abstracted from the set to quantify. The quantified values of the three attributes are fused by the influence degree and optimization degree model

and the cooperative game theory is used to optimize the edge side to simplify the indicator set

so as to effectively remove the redundancy of the edge side data. Secondly

based on the CRITIC (criteria importance though intercrieria correlation) weight calculation method

the data information of the index is fully utilized

and a more objective weight coefficient is given to the index proposed in this method. Finally

the absolute ideal solution is constructed by improving the grey TOPSIS (technique for order preference by similarity to an ideal solution) evaluation method to effectively avoid the reverse ranking problem caused by the dynamic change of the number of users. The introduced grey correlation degree can make up for the defect that the European criterion cannot accurately measure the advantages and disadvantages of users in the traditional method. The experimental results show that the proposed edge energy efficiency evaluation method not only greatly reduces the demand for data storage

but also fully guarantees the reliability and robustness of the evaluation results

which has obvious advantages in reducing the scale of data upload and quickly completing user energy efficiency evaluation.