This paper conducts a literature survey of relevant power consumption models for 5G cellular network base stations and provides a comparison of the models. . However, there is still a need to understand the power consumption behavior of state-of-the-art base station architectures, such as multi-carrier active antenna units (AAUs), as well as the impact of different network parameters. And through this, a multi-faceted assessment criterion that considers both economic and ecological factors is established. In this paper, firstly, an energy consumption prediction model based on long and short-term. . 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 such a model, accurate but simple to use.
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