Mayhorn Ebony, Butzbaugh Joshua, Meier Alan
Pacific Northwest National Laboratory, Richland, WA 99354, USA.
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Sensors (Basel). 2023 Oct 5;23(19):8253. doi: 10.3390/s23198253.
Evaluations of nonintrusive load monitoring (NILM) algorithms and technologies have mostly occurred in constrained, artificial environments. However, few field evaluations of NILM products have taken place in actual buildings under normal operating conditions. This paper describes a field evaluation of a state-of-the-art NILM product, tested in eight homes. The match rate metric-a technique recommended by a technical advisory group-was used to measure the NILM's success in identifying specific loads and the accuracy of the energy consumption estimates. A performance assessment protocol was also developed to address common issues with NILM mislabeling and ground-truth comparisons that have not been sufficiently addressed in past evaluations. The NILM product's estimates were compared to the submetered consumption of eight major appliances. Overall, the product had good performance in disaggregating the energy consumption of the electric water heaters, which included both electric resistance and heat-pump water heaters, but only a fair accuracy with refrigerators, dryers, and air conditioners. The performance was poor for cooking equipment, furnace fans, clothes washers, and dishwashers. Moreover, the product was often unable to detect major loads in homes. Typically, two or more appliances were not detected in a home. At least two dryers, furnace fans, and air conditioners went undetected across the eight homes. On the other hand, the dishwasher was detected in all homes where available or monitored. The key findings were qualitatively compared to those of past field evaluations. Potential areas for improvement in NILM product performance were determined along with areas where complementary technologies may be able to aid in load-disaggregation applications.
非侵入式负载监测（NILM）算法和技术的评估大多是在受限的人工环境中进行的。然而，很少有对NILM产品的现场评估是在正常运行条件下的实际建筑物中进行的。本文描述了对一种先进的NILM产品在八个家庭中进行的现场评估。匹配率指标——一个技术咨询小组推荐的技术——被用来衡量NILM在识别特定负载方面的成功率以及能耗估计的准确性。还制定了一个性能评估协议，以解决过去评估中未充分解决的NILM错误标记和实际情况比较等常见问题。将NILM产品的估计值与八个主要电器的子计量能耗进行了比较。总体而言，该产品在分解电阻式和热泵式电热水器的能耗方面表现良好，但对冰箱、烘干机和空调的准确性一般。对于烹饪设备、炉扇、洗衣机和洗碗机，其性能较差。此外，该产品经常无法检测到家庭中的主要负载。通常，一个家庭中会有两个或更多电器未被检测到。在这八个家庭中，至少有两个烘干机、炉扇和空调未被检测到。另一方面，洗碗机在所有可用或被监测的家庭中都被检测到了。将关键发现与过去的现场评估结果进行了定性比较。确定了NILM产品性能潜在的改进领域以及互补技术可能有助于负载分解应用的领域。
