ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (3): 704-715.doi: 10.7544/issn1000-1239.2016.20150762

• 其他应用技术 • 上一篇    下一篇

基于可穿戴设备感知的智能家居能源优化

陈思运1,刘烃1,沈超1,苏曼1,高峰2,徐占伯3,师嘉悦1,贾战培1   

  1. 1(智能网络与网络安全教育部重点实验室(西安交通大学) 西安 710049); 2(机械制造系统工程国家重点实验室(西安交通大学) 西安 710049); 3(加州大学伯克利分校伯克利教育联盟新加坡研究所 新加坡 新加坡城 138602) (tingliu@mail.xjtu.edu.cn)
  • 出版日期: 2016-03-01
  • 基金资助: 
    国家自然科学基金项目(61473218,61221063,U1301254);国家“八六三”高技术研究发展计划基金项目(2012AA011003)

Smart Home Energy Optimization Based on Cognition of Wearable Devices Sensor Data

ChenSiyun1,LiuTing1,ShenChao1,SuMan1,GaoFeng2,XuZhanbo3,ShiJiayue1,JiaZhanpei1   

  1. 1(Key Laboratory for Intelligent Networks and Network Security (Xi’an Jiaotong University), Ministry of Education, Xi’an 710049); 2(State Key Laboratory for Manufacturing System Engineering (Xi’an Jiaotong University), Xi’an 710049); 3(Berkeley Education Alliance Research in Singapore (University of California at Berkeley), Singapore City, Singapore 138602)
  • Online: 2016-03-01

摘要: 智能家居能源优化作为智能电网在居民侧的延伸是智能家居领域的重要分支.智能家居能源优化的目标是通过优化调度家居用电设备,满足用户的舒适需求和降低用电费用.其中,用户舒适度与人的行为密切相关,具有很强的主观性和不确定性,对用户行为及舒适度需求的分析是智能家居能源管理系统中的难点.因此提出了一种基于可穿戴设备传感数据分析的智能家居能源优化方法,主要包括:基于可穿戴设备传感器数据实时分析用户行为;利用神经网络建立用户行为到舒适度需求的映射,更新用户的舒适度需求;建立家居系统动态模型,并基于智能家居环境传感器数据对模型参数进行动态估计;提出基于模型预测控制(model predictive control, MPC)的智能家居能源优化求解方法.同时开发了智能家居能源优化的原型系统,通过搭建的智能家居实验平台,设计了4种典型用户行为情景,验证了所提方法对智能家居经济性和舒适性的提升.

关键词: 智能家居, 能源优化, 用户行为分析, 可穿戴设备, 智能电网

Abstract: As the extension of smart grid in demand side, smart home energy optimization is an important branch of smart home. Smart home energy optimization aims to optimally schedule the home appliances to satisfy the comfort requirements and save the electricity cost. However, the comfort requirements are closely related to the human behavior, which has great subjectivity and uncertainty. Thus profiling the comfort requirements is one of the challenging problems. This paper presents a smart home energy management method based on the sensors data of smart wearable devices, which contains the human behavior analysis; updates the comfort requirements through creating the mapping model between human behavior and the comfort requirements by neural network; establishes the system dynamic models; and the parameters are estimated by using the sensor network data. Finally, the smart home energy optimization is solved by model predictive control. Based on the proposed method, the smart home platform is set up and the smart home energy optimization systems are developed to support the smart phone. The experiment presents promising performance on electricity cost saving and comfort improvement in four scenarios of user behaviors.

Key words: smart home, energy optimization, human behavior analysis, smart wearable devices, smart grid

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