Can LSTM predict the daily load changes of base stations?
In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations.
How does a base station use ran power?
Traditional base stations handled baseband processing locally, thereby, collectively consuming more power. The radio unit is by far the most significant contributor to the total RAN power consumption power in a base station.
What is a base station load forecasting model?
Reference (Qu et al., ) introduces a base station load forecasting model that leverages spatio-temporal characteristics. To achieve this, a clustering algorithm based on artificial neural networks is employed to establish specific models for various types of base stations.
What is 5G base station load forecasting technology?
The research on 5G base station load forecasting technology can provide base station operators with a reasonable arrangement of energy supply guidance, and realize the energy saving and emission reduction of 5G base stations.
What are the standardized energy-saving metrics for a base station?
(1) Energy-saving reward: after choosing a shallower sleep strategy for a base station, the system may save more energy if a deeper sleep mode can be chosen, and in this paper, the standardized energy-saving metrics are defined as (18) R i e = E S M = 0 E S M = i E S M = 0 E S M = 3
How much energy does a communication base station use?
In this region, the communication base stations are equipped with energy storage systems with a rated capacity of 48 kWh and a maximum charge/discharge power of 15.84 kW. The self-discharge efficiency is set at 0.99, and the state of charge (SOC) is allowed to range between a maximum of 0.9 and a minimum of 0.1. Figure 3.
Energy-saving control strategy for ultra-dense network base
Aiming at the problem of mobile data traffic surge in 5G networks, this paper proposes an effective solution combining massive multiple-input multiple-output techniques
Energy Management Strategy for Distributed Photovoltaic 5G
Proposing a priority-based energy management strategy that dynamically optimizes and coordinates the energy flow of base stations based on factors such as photovoltaic
Coordinated scheduling of 5G base station energy
In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations.
A Holistic Study of Power Consumption and Energy Savings
In a study conducted in / with 31 operators1, for base-stations without air conditioning, 67% of energy consumption is in the radio, and only 10% of the energy is consumed in the BBU.
Optimization Control Strategy for Base Stations Based on
Therefore, in response to the impact of communication load rate on the load of 5G base stations, this paper proposes a base station energy storage auxiliary power grid peak shaving method
Base Station Energy Management in 5G Networks
The proposed Wide range of control for base station in green cellular network using sleep mode for switch (WGCNS) algorithm toon and off the base station will work in heavy load with neighbouring base station.
Energy-saving control strategy for ultra-dense network base stations
Aiming at the problem of mobile data traffic surge in 5G networks, this paper proposes an effective solution combining massive multiple-input multiple-output techniques
Energy Management Strategy for Distributed Photovoltaic 5G Base Station
Proposing a priority-based energy management strategy that dynamically optimizes and coordinates the energy flow of base stations based on factors such as photovoltaic
Coordinated scheduling of 5G base station energy storage for
In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load
Optimization Control Strategy for Base Stations Based on Communication Load
Therefore, in response to the impact of communication load rate on the load of 5G base stations, this paper proposes a base station energy storage auxiliary power grid peak shaving method
Base Station Energy Management in 5G Networks Using Wide
The proposed Wide range of control for base station in green cellular network using sleep mode for switch (WGCNS) algorithm toon and off the base station will work in heavy load with
Power Base Stations Load Management | HuiJue Group E-Site
In June , Reliance Jio partnered with Tata Power to deploy hybrid stations combining load management software with solar-diesel hybrids. The pilot achieved 54% grid independence
Base Station Microgrid Energy Management in 5G Networks
The 5G BSs powered by microgrids with energy storage and renewable generation can significantly reduce the carbon emissions and operational costs. The base
Modeling and aggregated control of large-scale 5G base stations
Specifically, the study focuses on optimizing traffic load spatial redistribution and maximizing renewable energy utilization to minimize grid energy consumption.
Energy Management of Base Station in 5G and B5G: Revisited
To achieve low latency, higher throughput, larger capacity, higher reliability, and wider connectivity, 5G base stations (gNodeB) need to be deployed in mmWave. Since mmWave
Energy-saving control strategy for ultra-dense network base stations
Aiming at the problem of mobile data traffic surge in 5G networks, this paper proposes an effective solution combining massive multiple-input multiple-output techniques
Energy Management of Base Station in 5G and B5G: Revisited
To achieve low latency, higher throughput, larger capacity, higher reliability, and wider connectivity, 5G base stations (gNodeB) need to be deployed in mmWave. Since mmWave