Data Flow Forecasting for Smart Grid Based on Multi-Verse Expansion Evolution Physical–Social Fusion Network

Data Flow Forecasting for Smart Grid Based on Multi-Verse Expansion Evolution Physical–Social Fusion Network

The accurate forecasting of financial flow data in power-grid operations is critical for improving operational efficiency. To tackle the challenges of low forecasting accuracy and high error rates caused by the long sequences, nonlinearity, and multi-scale and non-stationary characteristics of finan...

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Bibliographic Details
Main Authors: Kun Wang, Bentao Hu, Jiahao Zhang, Ruqi Zhang, Hongshuo Zhang, Sunxuan Zhang, Xiaomei Chen
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Energies
Subjects:
power grid financial flow
data forecasting
multi-verse expansion evolution
deep learning
spatial–temporal fusion network
Online Access:https://www.mdpi.com/1996-1073/18/12/3093
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Summary:The accurate forecasting of financial flow data in power-grid operations is critical for improving operational efficiency. To tackle the challenges of low forecasting accuracy and high error rates caused by the long sequences, nonlinearity, and multi-scale and non-stationary characteristics of financial flow data, a forecasting model based on multi-verse expansion evolution (MVE<sup>2</sup>) and spatial–temporal fusion network (STFN) is proposed. Firstly, preprocess data for power-grid financial flow data based on the autoregressive integrated moving average (ARIMA) model. Secondly, establish a financial flow data forecasting framework using MVE<sup>2</sup>-STFN. Then, a feature extraction model is developed by integrating convolutional neural networks (CNN) for spatial feature extraction and bidirectional long short-term memory networks (BiLSTM) for temporal feature extraction. Next, a hybrid fine-tuning method based on MVE<sup>2</sup> is proposed, exploiting its global optimization capability and fast convergence speed to optimize the STFN parameters. Finally, the experimental results demonstrate that our approach significantly reduces forecasting errors. It reduces RMSE by 5.75% and 13.37%, MAPE by 22.28% and 41.76%, and increases R<sup>2</sup> by 1.25% and 6.04% compared to CNN-BiLSTM and BiLSTM models, respectively. These results confirm the model’s effectiveness in improving both accuracy and efficiency in financial flow data forecasting for power grids.
ISSN:1996-1073

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