Non-intrusive load monitoring (NILM) is the task of determining the appliances individual contributions to the aggregate power consumption by using a set of electrical parameters measured at a single metering point. NILM allows to provide detailed consumption information to the users, that induces them to modify their habits towards a wiser use of the electrical energy. This paper proposes a NILM algorithm based on the joint use of active and reactive power in the Additive Factorial Hidden Markov Models framework. In particular, in the proposed approach, the appliance model is represented by a bivariate Hidden Markov Model whose emitted symbols are the joint active-reactive power signals. The disaggregation is performed by means of an alternative formulation of the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm for dealing with the bivariate HMM models. The proposed solution has been compared to the original AFAMAP algorithm based on the active power only and to the seminal approach proposed by Hart (1992), based on finite state machine appliance models and which employs both the active and reactive power. Hart’s algorithm has been improved for handling the occurrence of multiple solutions by means of a Maximum A Posteriori technique (MAP). The experiments have been conducted on the AMPds dataset in noised and denoised conditions and the performance evaluated by using the $F_1$-Measure and the normalized disaggregation metrics. In terms of $F_1$-Measure, the results showed that the proposed approach outperforms AFAMAP, Hart’s algorithm, and Hart’s with MAP respectively by $+14.9\%$, $+21.8\%$, and $+2.5\%$ in the 6 appliances denoised case study. In the 6 appliances noised case study, the relative performance improvement is $+25.5\%$, $+51.1\%$, and $+6.7\%$.

非侵入式负载监控（NILM）是通过使用在单个计量点测量的一组电气参数来确定设备对总功耗的单独贡献的任务。NILM允许向用户提供详细的消耗信息，从而促使他们改变习惯，以更明智地使用电能。本文在可加因数隐马尔可夫模型框架中提出了一种基于有功和无功联合使用的NILM算法。特别地，在所提出的方法中，设备模型由双变量隐式马尔可夫模型表示，该双变量隐马尔可夫模型的发射符号是联合的有功-无功功率信号。通过用于处理双变量HMM模型的可加因式近似最大后验（AFAMAP）算法的替代公式来执行分解。所提出的解决方案已与仅基于有功功率的原始AFAMAP算法进行了比较，并与基于有限状态机设备模型并采用有功和无功的Hart（1992）提出的开创性方法进行了比较。Hart算法已得到改进，可通过最大后验技术（MAP）处理多个解的出现。实验是在噪声和去噪条件下对AMPds数据集进行的，并通过使用 所提出的解决方案已与仅基于有功功率的原始AFAMAP算法进行了比较，并与基于有限状态机设备模型并采用有功和无功的Hart（1992）提出的开创性方法进行了比较。Hart算法已得到改进，可通过最大后验技术（MAP）处理多个解的出现。实验是在噪声和去噪条件下对AMPds数据集进行的，并通过使用 所提出的解决方案已与仅基于有功功率的原始AFAMAP算法进行了比较，并与基于有限状态机设备模型并采用有功和无功的Hart（1992）提出的开创性方法进行了比较。Hart算法已得到改进，可通过最大后验技术（MAP）处理多个解的出现。实验是在噪声和去噪条件下对AMPds数据集进行的，并通过使用$F_{\mathrm{1个}}$-测量和归一化分类指标。按照$F_{\mathrm{1个}}$-措施，结果表明，与MAP相比，该方法的性能优于AFAMAP，Hart算法和Hart算法。 $+14.9％$， $+21.8％$， 和 $+2.5％$在这6种去噪的设备中进行了案例研究。在6个受噪声干扰的案例研究中，相对性能的提高是$+25.5％$， $+51.1％$， 和 $+6.7％$。