Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base
This paper coordinates 5G BSs and sequential load restoration for DS resilience enhancement, and innovatively characterizes the decision-dependent behaviors of 5G BSs in the
According to the energy consumption characteristics of the base station, a 5G base station energy consumption prediction model based on the LSTM network is constructed to provide data support for
This project demonstrates the application of machine learning techniques in predicting energy consumption for 5G base stations. The results obtained from the XGBoost regression model indicate
In this thesis linear regression is compared with the gradient boosted trees method and a neural network to see how well they are able to predict energy consumption from field data of 5G radio base stations.
This paper proposes an electric load demand model of the 5th generation (5G) base station (BS) in a distribution system based on data flow analysis. First, the electric load model of a 5G BS is
In this paper, hourly electric load profiles of 5G BSs in residential, shopping, and office areas for future 5G application are simulated to compare and investigate their characteristics based
In this work, we present a novel analytical methodology to evaluate the EC of a 5G BS under varying traffic load.
Power consumption models for base stations are briefly discussed as part of the development of a model for life cycle assessment. An overview of relevant base station power
To ensure the safe and stable operation of 5G base stations, it is essential to accurately pre-dict their power load. However, current short-term prediction methods are rarely applied rationally in pertinent
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