摘要
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.
Base station is a major node for communication networks 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 par- ticle 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 dy- namic 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 energyconsumption model shows high prediction accuracy a uation of energy-saving engineering of the base station.
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2017年第2期122-128,共7页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金资助项目(61440026
51674113)
湖南省自然科学基金重点资助项目(13JJA002)~~
关键词
通信基站
能耗模型
最小二乘支持向量机
粒子群
滚动时间窗
base station
energy consumption mo PSO（Particle Swarm Optimization）
sliding window nd generalization ability, and is applicable for the eval- On. del
LSSVM（Least Squares Support Vector Machine）
