In this paper, we present a power consumption model for 5G AAUs based on artificial neural networks..
In this paper, we present a power consumption model for 5G AAUs based on artificial neural networks..
To this end, we develop a new model of a propulsion energy consumption for the UAV-BSs reflecting an impact of wind. Furthermore, we propose a novel algorithm based on an ensemble learning optimizing the 3D trajectory of UAV-BSs over time in realistic environment with wind to reduce the propulsion. .
In recent years, the design of new methods for decreasing the RAN power consumption has attracted interest from both the research community and standardization bodies, and many energy savings solutions have been proposed. However, there is still a need to understand the power consumption behavior. .
Cellular base stations consume a lot of energy since it requires a 24-h continuous power supply which results in an increased operational expenditure (OPEX) and environmental pollution. This OPEX and harmful effects should be decreased to achieve sustainable and profitable businesses for mobile. .
Numerous studies have affirmed that the incorporation of distributed photovoltaic (PV) and energy storage systems (ESS) is an effective measure to reduce energy consumption from the utility grid. The optimization of PV and ESS setup according to local conditions has a direct impact on the economic. .
This study introduces a predictive modeling approach for base station energy consumption by combining Seasonal and Trend decomposition using Loess (STL) and Long Short-Term Memory (LSTM) networks. The methodology involves decomposing the historical data into seasonal, trend, and residual components.
