Abstract: Carbon emission prediction is crucial for several aspects,including international cooperation,addressing climate change,and energy security.Due to the numerous and complex factors affecting carbon emission prediction within national geographic area and over longer time interval,there are higher requirements for the feature representation learning ability of prediction mo-dels.Aiming to the above problems,a carbon emission prediction model that integrates transformer,long short-term memory(LSTM) neural network,and generative adversarial network(GAN) is proposed,called TransLSTM-GAN.In this work,attention mechanism and high-performance feature representation learning ability are utilized via Transformer and LSTM network to improve the model learning ability in processing complex carbon emission data and long sequence data.An adaptive improved whale optimization algorithm(IWOA) is designed to automatic hyperparameter learning for TransLSTM,reducing training difficulty and improving training effectiveness.Using pre-trained TransLSTM as a generator and deep residual network(ResNet) as a discriminator,a GAN is constructed to fine tune the generator parameters and further improve prediction accuracy.To validate the performance of this model,experimental verification on carbon emission datasets in China,America,and Europe.The experimental results indicate that the TransLSTM-GAN model can better adapt to national regions and predict long-term carbon emissions.

Key words: TransLSTM, GAN, Deep learning, Whale optimization algorithm, Carbon emission prediction