Zero Carbon Park Carbon Emission Prediction Method Based on Deep Reinforcement Learning of Carbon Data

In response to the difficulty in determining the trend of carbon emissions prediction in zero-carbon parks, this paper proposes a zero-carbon park carbon emission prediction method based on deep reinforcement learning of energy carbon data. This method first constructs a comprehensive energy system model for zero-carbon parks and collects and analyzes energy usage data and energy carbon data within the zerocarbon parks. Then, using deep reinforcement learning techniques, a model based on a convolutional neural network was constructed to predict the carbon emissions of the park. Finally, through continuous learning and optimization, the model can accurately predict the carbon emission trend of the park in the future period. The experimental results show that this method can significantly improve the accuracy of carbon emission prediction in zero-carbon parks, enhance the convergence speed of the zero-carbon park carbon emission prediction model, and enable the model to quickly converge to the optimal solution in a short period, providing accurate and reliable support for carbon emission management and emission reduction strategy formulation in parks.