The increased awareness regarding the impact of energy consumption on the environment has led to an increased focus on reducing energy consumption. Feedback on the appliance level energy consumption can help in reducing the energy demands of the consumers. Energy disaggregation techniques are used to obtain the appliance level energy consumption from the aggregated energy consumption of a house. These techniques extract the energy consumption of an individual appliance as features and hence face the challenge of distinguishing two similar energy consuming devices. To address this challenge we develop methods that leverage the fact that some devices tend to operate concurrently at specific operation modes. The aggregated energy consumption patterns of a subgroup of devices allows us to identify the concurrent operating modes of devices in the subgroup. Thus, we design hierarchical methods to replace the task of overall energy disaggregation among the devices with a recursive disaggregation task involving device subgroups. Experiments on two real-world datasets show that our methods lead to improved performance as compared to baseline. One of our approaches, Greedy based Device Decomposition Method (GDDM) achieved up to 23.8%, 10% and 59.3% improvement in terms of micro-averaged f score, macro-averaged f score and Normalized Disaggregation Error (NDE), respectively.
|Original language||English (US)|
|Title of host publication||e-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||11|
|State||Published - Jun 15 2019|
|Event||10th ACM International Conference on Future Energy Systems, e-Energy 2019 - Phoenix, United States|
Duration: Jun 25 2019 → Jun 28 2019
|Name||e-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems|
|Conference||10th ACM International Conference on Future Energy Systems, e-Energy 2019|
|Period||6/25/19 → 6/28/19|
Bibliographical noteFunding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.
© 2019 Association for Computing Machinery.