Evaluating deep learning architectures for CO2 emissions forecasting: TCN, LSTM, and hybrid approaches with hyperparameter optimization

The object of the study is CO2 emission prediction using deep learning models. The problem lies in developing accurate models capable of handling temporal dependencies and periodic patterns in CO2 data. To address this, three deep learning models – temporal convolutional network (TCN), long short-term memory (LSTM), and a hybrid TCN-LSTM are evaluated. These models are optimized using random search and Bayesian optimization. Results indicate that the Hybrid TCN-LSTM model, optimized via random search, performs best, achieving MAE: 1.0269, R2: 0.9305, and MAPE: 4.47%. TCN excels at capturing periodic patterns through dilated convolutions, while LSTM handles long-term dependencies. Their integration combines these strengths, improving accuracy. Optimal hyperparameters (learning rate: 0.000539, dropout rate: 0.5) enhance robustness. Random search outperforms Bayesian optimization in navigating complex search spaces and avoiding local optima. Key findings include the hybrid model's ability to address short-term periodicity and long-term trends, and Random Search’s reliability over Bayesian methods in this context. These insights advance time series forecasting methodologies and support robust predictive frameworks. Practically, they aid environmental policy, energy planning, and carbon trading by enabling data-driven decisions for emission reduction. However, implementation requires high-quality historical data and sufficient computational resources