Research on Carbon Emission Prediction for 5G Base Stations Based on a Hybrid BP-LSTM Model
Year: 2025
Type: article
Abstract: The rapid deployment and widespread adoption of 5G networks have rendered the energy consumption and carbon emissions of base stations increasingly prominent, posing a critical challenge for the telecommunications industry in achieving dual-carbon goals. To address the carbon emission prediction challenge in 5G base stations, this study proposes a hybrid forecasting model based on the deep integration of a Backpropagation (BP) neural network and Long Short-Term Memory (LSTM). By collecting multi-dimensional base station data encompassing equipment energy consumption, material usage, transmission coverage range, deployment configurations, and environmental conditions, systematic feature engineering and data preprocessing were conducted to construct a BP-LSTM hybrid model capable of capturing both static characteristics and temporal dynamics. Experimental results demonstrate that the proposed hybrid model achieves superior performance in 5G base station carbon emission prediction, with evaluation metrics reaching R² = 0.98 and MAPE = 3.25%, significantly outperforming individual models. Based on the prediction outcomes, this study further proposes multi-dimensional energy-saving optimization strategies tailored to diverse deployment environments and operational conditions.
Author: Yankai Chao
Institution: Jilin University
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410474688