An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.

本文提出了一种使用时间上下文信息对基线非侵入式负荷监测方法进行扩展的方法。详细地，所提出的方法使用具有特定于设备的时间上下文信息的两阶段分解方法，以便捕获低频数据集中的时变功耗模式。使用不同采样频率，设备数量和类型的数据集对提出的方法进行了评估。当使用特定于设备的时间上下文信息时，观察到的改进为1.5％，最高可达7.3％。借助两阶段分解架构并使用特定于设备的时间上下文信息，所有评估数据集的整体能量分解准确性都得到了进一步提高，并且观察到的最大改进是，