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<p>Neural network models have advantages in spatial and temporal information processing, which provide new ideas for the construction of customer-aware grid interactive service system. In this paper, firstly, the spatio-temporal fusion deep neural network model based on the attention mechanism is used to extract spatial and temporal information by convolutional neural network and gated recurrent neural network, respectively. The attention mechanism is also introduced to assign weights to various types of features to improve prediction accuracy. Secondly, the global information of the attention mechanism of the channel on the feature map is fully utilized to solve the problem of inadequate extraction of feature information from a single model, and then the grid interactive service system is constructed. The results show that the model proposed in this paper has higher prediction accuracy with root mean square error, coefficient of determination, and prediction accuracy of 0.972, 1.742, and 0.935, respectively. To verify the performance of the cache-based curve service in handling customer-perceived grid interactions, minute samples are created, where the maximum number of minute sample records is 3.5 million. Thus, it is shown that by introducing the attention mechanism can effectively improve the computational efficiency of the model, get better prediction results, and meet the demand for efficient and stable customer-aware interactive service system.</p>
</abstract>

Neural network models have advantages in spatial and temporal information processing, which provide new ideas for the construction of customer-aware grid interactive service system. In this paper, firstly, the spatio-temporal fusion deep neural network model based on the attention mechanism is used to extract spatial and temporal information by convolutional neural network and gated recurrent neural network, respectively. The attention mechanism is also introduced to assign weights to various types of features to improve prediction accuracy. Secondly, the global information of the attention mechanism of the channel on the feature map is fully utilized to solve the problem of inadequate extraction of feature information from a single model, and then the grid interactive service system is constructed. The results show that the model proposed in this paper has higher prediction accuracy with root mean square error, coefficient of determination, and prediction accuracy of 0.972, 1.742, and 0.935, respectively. To verify the performance of the cache-based curve service in handling customer-perceived grid interactions, minute samples are created, where the maximum number of minute sample records is 3.5 million. Thus, it is shown that by introducing the attention mechanism can effectively improve the computational efficiency of the model, get better prediction results, and meet the demand for efficient and stable customer-aware interactive service system.

Construction of customer-aware grid interactive service system based on neural network model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Neural network models have advantages in spatial and temporal information processing, which provide new ideas for the construction of customer-aware grid...

Construction of customer-aware grid interactive service system based on neural network model

Pubblicato online: 02 ott 2023

Pagine: -

Ricevuto: 10 gen 2023

Accettato: 12 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00436

Parole chiave
Convolutional neural network, Gated recurrent neural network, Attention mechanism, Perceptual grid, Interactive services

© 2023 Baowei Zhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction of customer-aware grid interactive service system based on neural network model

Pubblicato online: 02 ott 2023

Pagine: -

Ricevuto: 10 gen 2023

Accettato: 12 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00436

Parole chiaveConvolutional neural network, Gated recurrent neural network, Attention mechanism, Perceptual grid, Interactive services

© 2023 Baowei Zhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Convolutional neural network, Gated recurrent neural network, Attention mechanism, Perceptual grid, Interactive services