Material Consumption Prediction of Modern Intelligent Supply Chain for Electric Grid Based on Deep Learning

Predicting material consumption in modern intelligent supply chains for electric grids is crucial for optimizing inventory management and ensuring efficient operations. Traditional forecasting methods often struggle with the complexity and non-linearity of supply chain dynamics, especially in the context of rapidly evolving technologies and fluctuating demand patterns. This paper proposes a novel approach utilizing deep learning techniques to forecast material consumption accurately. First, the material programme management framework is analysed. Then, a networked warehousing system is constructed to storage and dispatch materials. In addition, an efficient material inventory management approach is proposed. Next, based on long short-term memory (LSTM) neural network in deep learning technology, a material consumption prediction model is built to accurately predict material consumption, which helps for the modern intelligent supply chain construction of electric grid businesses. Finally, some practical examples exhibit that the established material consumption prediction model can accurately predict material consumption. This research underscore the potential of deep learning in revolutionizing supply chain management for electric grids, paving the way for more resilient and sustainable energy infrastructures.