Improved SVM-LSTM-based resource flow forecasting for the low-carbon urban distribution grid - Sun, Lei (Frontiers in Energy Research, 2024)
Resource flow supports the delivery of products and services and plays a vital role in low-carbon urban distribution grid. Therefore, reasonable forecasting of resource flow is essential for financial decision-making. Through the trained model, the resource flow forecasting process can be simplified and one-click forecasting can be realized. However, this method relies on the selection and optimization of model parameters, where poor parameter choices can significantly impact forecasting accuracy. This paper first introduces a model for identifying key influencing factors in resource flow data, incorporating an elastic network and gray correlation analysis. Subsequently, a resource flow forecasting method based on improved support vector machines-long short-term memory (SVM-LSTM) is proposed. Finally, the superior performance of the proposed method is validated through simulations.