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• W4386543324 abstract "This paper investigates smart grid energy supply forecasting and economic operation management, with a focus on building an efficient energy supply prediction model. Four datasets were selected for training, and a Snake Optimizer (SO) algorithm-optimized Bigru-Attention model was proposed to construct a comprehensive and efficient prediction model, aiming to enhance the reliability, sustainability, and cost-effectiveness of the power system. The research process includes data preprocessing, model training, and model evaluation. Data preprocessing ensures data quality and suitability. In the model training phase, the Snake Optimizer (SO) algorithm-optimized Bigru-Attention model combines time series, spatial features, and optimization features to build a comprehensive prediction model. The model evaluation phase calculates metrics such as prediction error, accuracy, and stability, and also examines the model’s training time, inference time, number of parameters, and computational complexity to assess its efficiency and scalability. The contribution of this research lies in proposing the Snake Optimizer (SO) algorithm-optimized Bigru-Attention model and constructing an efficient comprehensive prediction model. The results indicate that the Snake Optimizer (SO) algorithm exhibits significant advantages and contributes to enhancing the effectiveness of the experimental process. The model holds promising applications in the field of energy supply forecasting and provides robust support for the stable operation and optimized economic management of smart grids. Moreover, this study has positive social and economic implications for the development of smart grids and sustainable energy utilization." @default.
• W4386543324 created "2023-09-09" @default.
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• W4386543324 date "2023-09-08" @default.
• W4386543324 modified "2023-10-16" @default.
• W4386543324 title "Intelligent power grid energy supply forecasting and economic operation management using the snake optimizer algorithm with Bigur-attention model" @default.
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• W4386543324 doi "https://doi.org/10.3389/fenrg.2023.1273947" @default.
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