山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (4): 86-94.doi: 10.6040/j.issn.1672-3961.0.2023.287
• 机器学习与数据挖掘 • 上一篇
王凤娟1,王语睿2,卫兰3,4*,范存群3,4,徐晓斌2
WANG Fengjuan1, WANG Yurui2, WEI Lan3,4*, FAN Cunqun3,4, XU Xiaobin2
摘要： 针对环境大数据在智慧城市应用中的实时性和准确性问题,提出一种基于自适应线性模型的环境数据预测算法。根据气象数据的实时变化情况对模型进行训练,自适应调整训练窗口大小,并在训练态与预测态之间动态实时切换,使模型具有较强的适应环境的能力。该算法具有较低的时延和较小的计算开销,可以在传感器节点上直接部署,满足数据预测的实时性需求。在真实环境数据集的基础上构建仿真试验,相比固定窗口模型,该算法数据预测误差降低17.4%以上,环境数据采集能耗降低80%以上,平均时延降低超过50%;相比已有的机器学习算法,训练及预测时间降低37%以上。
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