Affiliations: [a]
Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| [b]
Fellow SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Interdisciplinary Research Center of Intelligent Secure Systems, Information and Computer Science Department, Computer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Correspondence:
[*]
Corresponding author. El-Sayed M. El-Alfy, Fellow SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Interdisciplinary Research Center of Intelligent Secure Systems, Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 34464, Saudi Arabia. E-mail: [email protected].
Abstract: Energy is a critical resource for daily activities and lifestyles with direct impacts on the economy, health and environment. Therefore, monitoring its efficient use is essential to reduce energy waste and lessen related concerns such as global warming and climate change. One of the prominent and evolving solutions is Non-Intrusive Load Monitoring (NILM) smart meters, which enables consumers to track their per-appliance energy consumption more effectively. Some recent approaches have proposed deep learning as a powerful tool for energy disaggregation. However, it is difficult to employ these models in resource-constrained end devices for effective energy monitoring. In this paper, we explore and evaluate a lightweight improved model for multi-target non-intrusive load monitoring based on MobileNet architectures. With extensive experiments using the ENERTALK dataset, the results show that MobileNetV3-large is the most appealing for energy disaggregation as it requires about 55% less storage for trained model and about 6% less training time than MobileNetV2 with almost the same performance. On average, version 3 large has a 17.63% reduction in SAE and requires 54.21% and 8.93% less space and less training time than version 2, respectively. Moreover, the average performance is boosted using an ensemble multi-target MobileNet model across all houses, leading to significant reduction of MAE, SAE, and RMSE errors of about 6%, 48%, and 4%, respectively. In comparison to other work, the proposed MMNet-NILM shows superior performance for the majority of appliances in terms of all considered evaluation metrics.
Keywords: Multi-target MobileNet, ENERTALK, Lightweight NILM, energy disaggregation, ensemble MobileNet
