Electric power material supply demand prediction based on multi-data fusion

In the power industry, accurate prediction of material supply demand plays a crucial role in the effective planning and efficient operation of the power system. This prediction not only affects the timely maintenance and updating of power equipment, but also directly relates to the stability and safety of power supply. However, when traditional methods are used to predict the demand for power supplies, due to factors such as single data source and simple model, large prediction errors often exist, which is difficult to meet the strict requirements of the power industry for prediction accuracy. To solve this problem, this paper proposes a method for predicting the demand for power material supply based on multi data fusion. This method not only integrates data from multiple channels and types, but also cleverly combines advanced artificial intelligence (AI) algorithms to fully explore and utilize hidden information and patterns in the data. The experimental results show that compared with traditional prediction methods, the multi data fusion-based prediction method proposed in this paper significantly improves the prediction accuracy. This method can more accurately capture the changing trends and periodic characteristics of electricity material demand, effectively reducing prediction errors.