An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives.

中文翻译：

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基于优化的低频智能电表数据能量分解算法

提出了一种将能耗非侵入式分解为最终用途的算法，也称为非侵入式设备负荷监控（NIALM）。该算法解决了一个优化问题，其目标是使总能耗与每个设备的各个贡献之和之间的误差最小。该算法假定家庭中存在的一部分负载是已知的（例如洗衣机，洗碗机等），但是它也考虑未知负载，将它们视为单个负载。然后将算法的性能与通过可公开获得的NILMTK框架中实施的两种最新分类方法所获得的性能进行比较。第一个基于组合优化，第二个基于因子隐马尔可夫模型。