Fusion model with attention mechanism for carbon-neutral sports competitions - Zhang, Jun; Zhang, Xuan (Frontiers in Ecology and Evolution, 2023)
In this paper, we propose a CNN-GRU based on an attention mechanism for a prediction model of energy saving and emission reduction scheme for sports competitions under carbon neutral target, which effectively solves the problem of gradient disappearance and gradient explosion, and at the same time improves the accuracy and practicality of the model. Multidimensional CNN has strong multi-channel input data processing capability. First, there are different energy-saving and emission-reduction schemes for a sports competitions match to achieve carbon-neutral targets. First, the data of other energy-saving and emission-reduction schemes for achieving the carbon-neutral goal of the sports competitions match is input into the multidimensional CNN network. After convolution for feature extraction, it enters the pooled simplified feature data. Then the simplified feature data is input into the GRU network with the attention layer based on one of the LSTM extensions. The GRU model introduces the gating mechanism to better capture the long-range dependencies in the time series. sports competitions with more efficient efficiency in predicting other energy-saving and emission-reduction options.