Digital Twins Fuzzy System Based on Time Series Forecasting Model LFTformer

Currently, people are actively seeking new and clean energy sources to replace traditional fossil fuels. As a widely distributed, abundant, and easily accessible renewable energy source, wind energy has multiple applications both domestically and internationally, and has a promising future. In wind power forecasting, this paper proposes a new algorithm LFTformer that combines the Transformer model with linear fuzzy information granulation (LFIG).The model uses the improved LFIG algorithm to extract the semantic information of time series, and divides the original time series into multiple information granules, which are then used as inputs to the Transformer model. Through comparative experiments for wind power prediction over 72 hours, 120 hours, and 168 hours, this research demonstrates that the LFTformer can improve the accuracy of wind power prediction. Finally, a Digital Twins system for wind power prediction is constructed using three-dimensional visualization techniques, displaying the prediction results and key data indicators, providing reliable data support for wind power systems.