基于滚动时间窗的PSO-LSSVM的通信基站能耗建模

基站是通信网络的重要能耗节点,精准计算合同能源管理(EPC)模式下基站节能量成为该领域的技术瓶颈.以3类典型场景通信基站为对象,提出了一种基于粒子群优化算法(PSO)的滚动时间窗最小二乘支持向量机(LSSVM)的基站能耗建模方法.该方法通过选取预处理的基站配置参数与实时数据建立滚动时间窗,采用PSO优化训练模型参数,并通过LSSVM回归估计训练模型,得到随时间窗数据变化的基站动态能耗模型.仿真试验与样本基站实测数据的验证结果表明,本文建立的能耗模型具有较高的预测精度及泛化能力,对基站节能工程的评估具有良好的应用前景.

Modelling of Base Station Energy Consumption System Based on Sliding Window PSO-LSSVM

Base station is a major node for communication network''s energy consumption. The accurate calculation of the energy-saving amount for the base station under EPC model is a technology bottleneck in this field. This paper proposed a modeling method of energy consumption of the base station based on particle swarm optimization (PSO) and least squares support vector machine (LSSVM) of sliding window, oriented at three kinds of typical scenarios base station. In this approach, a sliding window was established by selecting configuration parameters of base station and real-time data for pretreatment, and then the dynamic energy consumption model was obtained for the base station, which varied in accordance with that of the sliding window by means of the parameters for PSO training model and LSSVM regression training model. Compared with the simulation and test results from the sample base station, the proposed energy consumption model shows high prediction accuracy and generalization ability, and is applicable for the evaluation of energy-saving engineering of the base station.