Hybrid Control Strategy for 5G Base Station Virtual Battery
Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling
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 the subsequent BSES aggregation and collaborative scheduling.
In the optimal configuration of energy storage in 5G base stations, long-term planning and short-term operation of the energy storage are interconnected. Therefore, a two-layer optimization model was established to optimize the comprehensive benefits of energy storage planning and operation.
• The 5G base station energy consumption prediction model based on LSTM proposed in this paper takes into account the energy consumption characteristics of 5G base stations. The prediction results have high accuracy and provide data support for the subsequent research on BSES aggregation and optimal scheduling.
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.
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