一种非侵入式低功耗高精度的用电器识别算法

非侵入式用电器识别是实现泛在电力物联网客户侧智能感知的关键技术。针对现有非侵入式用电器识别系统计算复杂度过高,不利于产业化的问题,提出了一种基于长短期记忆网络的低成本、低功耗、高精度且可应用于嵌入式微控制器的非侵入式用电器识别算法和系统。首先,对总线上的电流进行同步采集;其次,利用所提出的多参数的检测方法判断出用电器的投切事件;再次,利用长短期记忆网络,对投切时间点前后变化的数据进行识别,得到投切事件的用电器种类;最后,结合累积和判断出当前的用电器的种类和数量。仿真和实测结果表明,只需要针对单个用电器进行少量数据的训练,即可在嵌入式微控制器系统中对组合用电器实现高达99.6%的平均识别准确度,且系统功耗小于1.5 W。这种算法可进一步应用于统计各个用电器的投切时间点、使用时长和功耗总和等工作情况,为智能电网提供精细化的用户用电信息,并为电网能源管理和优化提供重要参考。

Low-power consumption high-precision non-intrusive electrical appliance identification algorithm

Non-intrusive electrical appliance identification is the key technology to realize customer-side intelligent sensing in the ubiquitous power Internet of Things.Aiming at the problem that the calculation complexity of the existing non-intrusive electrical appliance identification system is too high and is not conducive to industrialization,a low-cost,low-power consumption,high-precision non-intrusive electrical appliance identification system and the algorithm based on the Long Short-Term Memory(LSTM) network are proposed,and they can be applied to an embedded microcontroller.First,the current on the bus is collected synchronously.Second,the proposed multi-parameter detection method is used to judge the switching event of the electrical appliance.Third,the LSTM network is used to process the data before and after the switching time point,and the type of electrical appliance which is switching is obtained.Finally,the current types and quantities of electrical appliances are judged by the cumulative sum.Simulation and measurement results show that only a small amount of data training for a single electric appliance is needed,and that the recognition accuracy of combined electric appliances can be up to 99.6% in the proposed embedded microcontroller system,in which the power consumption is less than 1.5 watts.The proposed algorithm can be further applied to the statistics of the switching time point,service time and total power consumption of each electrical appliance,which provides refined user power consumption information for the smart grid.and provides an important reference for the energy management and optimization in the smart grid.