Compared to Intrusive Load Monitoring which uses smart power meters at each level to be monitored, Non-Intrusive Load Monitoring (NILM) is an ingenious way that relies on signal readings at a single point to deduce the share of the devices that have contributed to the overall load. This reliable technique that guarantees the safety and privacy of individual users has recently become an increasingly popular topic, as it turns out to be a major solution to assist household users in the process of obtaining details of their electricity consumption. The detailed consumption promotes better management of the electrical power on the consumer side by helping to eliminate any waste of energy. In this paper, an edge gateway has been implemented to safely monitor the overall load in a smart energy system. A load separation method has been introduced based on events detected on a low-frequency power signal, which allows the consumption profile of On/Off and multi-state devices to be generated without relying on the knowledge of the cardinality of these devices Following the extraction of significant features contained in the aggregate signal, an appliance profile recognition approach is presented based on the non-parametric Mean Shift algorithm. The ability of the proposed method to learn and deduce devices profile is validated using the Reference Energy Disaggregation Dataset (REDD). The experimental results show that the proposed approach is efficient in detecting events of binary state and finite state appliances.

与在每个级别使用智能功率计进行监控的侵入式负载监控相比，非侵入式负载监控（NILM）是一种独创的方法，它依靠单点的信号读数来推算对负载产生影响的设备的份额。总负荷。这种可靠的技术可确保个人用户的安全和隐私，最近已成为越来越受欢迎的话题，因为它已成为协助家庭用户获取用电量详细信息的主要解决方案。详细的消耗量有助于消除能源浪费，从而促进了对用户方电力的更好管理。在本文中，已实施边缘网关以安全地监视智能能源系统中的总负载。已经基于在低频功率信号上检测到的事件引入了一种负载分离方法，该方法允许生成On / Off和多状态设备的功耗曲线，而无需依赖这些设备的基数知识针对聚合信号中包含的重要特征，提出了一种基于非参数均值漂移算法的设备配置文件识别方法。使用参考能量分解数据集（REDD）验证了所提出方法学习和推断设备配置文件的能力。实验结果表明，该方法能有效检测二进制状态和有限状态设备的事件。它允许在不依赖这些设备基数的知识的情况下生成开/关和多状态设备的功耗曲线，在提取了包含在聚合信号中的重要特征之后，提出了一种基于非参数均值漂移算法。使用参考能量分解数据集（REDD）验证了所提出方法学习和推断设备配置文件的能力。实验结果表明，该方法能有效检测二进制状态和有限状态设备的事件。它允许在不依赖这些设备基数的知识的情况下生成开/关和多状态设备的功耗曲线，在提取了包含在聚合信号中的重要特征之后，提出了一种基于非参数均值漂移算法。使用参考能量分解数据集（REDD）验证了所提出方法学习和推断设备配置文件的能力。实验结果表明，该方法能有效检测二进制状态和有限状态设备的事件。提出了一种基于非参数均值漂移算法的设备轮廓识别方法。使用参考能量分解数据集（REDD）验证了所提出方法学习和推断设备配置文件的能力。实验结果表明，该方法能有效检测二进制状态和有限状态设备的事件。提出了一种基于非参数均值漂移算法的设备轮廓识别方法。使用参考能量分解数据集（REDD）验证了所提出方法学习和推断设备配置文件的能力。实验结果表明，该方法能有效检测二进制状态和有限状态设备的事件。