Research on the application of LSTM and Transformer fusion model in time series prediction
Wang Shouchen1, Wang Limin2
1. School of Information Engineering,Hebei University of Architecture; 2. Faculty of Science,Hebei University of Architecture
Abstract： With the rapid development of renewable energy, wind power prediction is of great significance for the stable operation of the power grid and energy management. Wind power prediction is a complex nonlinear problem that involves multiple meteorological factors and environmental conditions. This article proposes a fusion model based on Long Short-Term Memory Network (LSTM), Adaptive Sparse Self-Attention Mechanism (ASSA), and Transformer for time series prediction of power generation. This model combines the advantages of LSTM in capturing long-term dependencies of time series, the efficiency of ASSA in handling local feature interaction sparsity, and the powerful parallel processing capability of Transformer in capturing global dependencies. Through experimental verification, the model performs well in power generation prediction tasks, especially in improving prediction accuracy at extreme fluctuations or inflection points. Compared with traditional methods, this model can more accurately capture the complexity and dynamics of wind power changes, providing strong decision support for the operation and management of wind farms.
Key words : Adaptive Sparse Self-Attention Mechanism (ASSA);LSTM；Transformer；time series;power prediction
