The increasing cost, energy demand, and environmental issues has led many researchers to find approaches for energy monitoring, and hence energy conservation. The emerging technologies of Internet of Things (IoT) and Machine Learning (ML) deliver techniques that have the potential to efficiently conserve energy and improve the utilization of energy consumption. Smart Home Energy Management Systems (SHEMSs) have the potential to contribute in energy conservation through the application of Demand Response (DR) in the residential sector. In this paper, we propose appliances Operation Modes Identification using Cycles Clustering (OMICC) which is SHEMS fundamental approach that utilizes the sensed residential disaggregated power consumption in supporting DR by providing consumers the opportunity to select lighter appliance operation modes. The cycles of the Single Usage Profile (SUP) of an appliance are extracted and reformed into features in terms of clusters of cycles. These features are then used to identify the operation mode used in every occurrence using K-Nearest Neighbors (KNN). Operation modes identification is considered a basis for many potential smart DR applications within SHEMS towards the consumers or the suppliers
翻译:不断增长的成本、能源需求和环境问题促使许多研究人员寻找能源监测方法,从而实现节能。新兴的物联网技术(IoT)和机器学习技术(ML)提供技术,具有高效节能和改善能源消费利用的潜力。智能家庭能源管理系统(SHEMS)具有通过在住宅部门应用需求反应(DR)对节能作出贡献的潜力。在本文件中,我们提议采用循环组合(OMICC)的电器操作模式识别方法(OMICC),这是SHEMS的基本方法,它利用有感的住宅分解电力消费支持DR,为消费者提供选择较轻的应用程序操作模式的机会。一种应用的单一使用概况(SUP)的周期被提取并改革为循环周期的特点。这些功能被用来确定在每一次情况下使用K-Nearest Neighbors(KNN)的操作模式。操作模式识别被认为是SHEMS的许多潜在智能DR应用程序的基础。