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

Kun Wang (), Bentao Hu, Jiahao Zhang, Ruqi Zhang, Hongshuo Zhang, Sunxuan Zhang and Xiaomei Chen
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Kun Wang: State Grid Jibei Electric Power Company, Beijing 100032, China
Bentao Hu: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Jiahao Zhang: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Ruqi Zhang: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Hongshuo Zhang: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Sunxuan Zhang: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Xiaomei Chen: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 12, 1-20

Abstract: 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 2 ) 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 2 -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 2 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 2 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.

Keywords: power grid financial flow; data forecasting; multi-verse expansion evolution; deep learning; spatial–temporal fusion network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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