Research Of Energy Consumption Prediction Model For Mobile Base Station

This paper based on the systematic analysis of the energy consumption ratio of mobile base station,according to the equipment type and energy consumption characteristics grouped the mobile base station,and the data drive the system: based on two typical scenarios with high proportion of applications in existing networks,used linear fitting,least squares method and sample analysis method to analyze the relationship between the number of radio frequency units and energy consumption,and the auxiliary equipment,because of energy consumption.The energy consumption prediction model of mobile base station is obtained by linear accumulation of grouping prediction model.The analysis method of the model realizes the correlation analysis between the static parameters of typical scenarios of sample base stations and the dynamic parameters of energy consumption data of mobile base stations.It improves the analysis and prediction methods used by operators in the current network,and solves the main problems of lacking the main equipment factors,the interrelated factors among the equipment and the influence of external environmental factors in the process of energy consumption prediction.In the process of verifying the scientific validity of the energy consumption analysis and prediction model,by comparing the predicted value of the energy consumption prediction model with the actual energy consumption value of the mobile base station,and by using the predicted value of the energy consumption prediction model as the threshold value of the threshold analysis method,the warning results of the traditional threshold analysis method are compared,the result of verification is known: the energy consumption forecasting model established in this paper is scientific and accurate,and is conducive to improving the efficiency of maintenance personnel and saving work costs in the process of energy consumption management,thus promoting the standardization process of energy conservation and emission reduction in the communications industry.