The invention discloses a 5G
base station micro-photovoltaic multi-
source data dynamic charging method and
system, and belongs to the technical field of 5G
base station charging. Multi-
source data (including a
base station operation state, weather forecast and a
regional power consumption task) are integrated, an LSTM and XGBoost fused fault prediction model, time convolutional network
power consumption demand prediction and a multi-target optimization model (optimizing
power grid power
purchase cost,
energy storage loss and photovoltaic
utilization rate) are adopted, a charging strategy is dynamically generated in combination with a cloud, and the
power consumption of the
power grid is optimized. And the strategy is adjusted in real
time based on the fault prediction result. According to the method, the optimized strategy is executed by remotely controlling the micro-photovoltaic equipment,
global energy scheduling driven by multi-dimensional data is realized, the photovoltaic
utilization rate is remarkably improved, meanwhile, through a fault feedforward mechanism and dynamic
weight adjustment, the operation cost is reduced on the basis of guaranteeing the power supply stability, and the power supply efficiency is improved. The problems of data island, response
lag, fault
vulnerability and the like in a traditional scheme are solved.