Energy efficiency optimization and carbon emission reduction targets of resource-based cities based on BiLSTM-CNN-GAN model

Introduction: Energy consumption and carbon emissions are major global concerns, and cities are responsible for a significant portion of these emissions. To address this problem, deep learning techniques have been applied to predict trends and influencing factors of urban energy consumption and carbon emissions, and to help formulate optimization programs and policies.

Methods: In this paper, we propose a method based on the BiLSTM-CNN-GAN model to predict urban energy consumption and carbon emissions in resource-based cities. The BiLSTMCNN-GAN model is a combination of three deep learning techniques: Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN). The BiLSTM component is used to process historical data and extract time series information, while the CNN component removes spatial features and local structural information in urban energy consumption and carbon emissions data. The GAN component generates simulated data of urban energy consumption and carbon emissions and optimizes the generator and discriminator models to improve the quality of generation and the accuracy of discrimination.

Results and discussion: The proposed method can more accurately predict future energy consumption and carbon emission trends of resource-based cities and help formulate optimization plans and policies. By addressing the problem of urban energy efficiency and carbon emission reduction, proposed method contributes to sustainable urban development and environmental protection.