Article
Efficient Design of Energy Disaggregation Model with
BERT-NILM Trained by AdaX Optimization Method for
Smart Grid
˙
Ismail Hakkı Çavdar and Vahit Feryad *
Citation: Çavdar,
˙
I.H.; Feryad, V.
Efficient Design of Energy
Disaggregation Model with
BERT-NILM Trained by AdaX
Optimization Method for Smart Grid.
Energies 2021, 14, 4649. https://
doi.org/10.3390/en14154649
Academic Editor: Abu-Siada Ahmed
Received: 16 July 2021
Accepted: 28 July 2021
Published: 30 July 2021
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Department of Electrical and Electronic Engineering, Karadeniz Technical University, Trabzon 61080, Turkey;
cavdar@ktu.edu.tr
* Correspondence: vahit.feryat@gmail.com; Tel.: +90-535-733-1609
Abstract:
One of the basic conditions for the successful implementation of energy demand-side
management (EDM) in smart grids is the monitoring of different loads with an electrical load
monitoring system. Energy and sustainability concerns present a multitude of issues that can
be addressed using approaches of data mining and machine learning. However, resolving such
problems due to the lack of publicly available datasets is cumbersome. In this study, we first
designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly
available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then
we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset
(TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the
proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with
sensors and Node-Red software installations were established to collect data in the research. In the
context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify
household appliances according to TEAD data. A highly accurate supervised ED is introduced,
which was designed to raise awareness to customers and generate feedback by demand without the
need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require
much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM
tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep
learning (DL) architecture based on bidirectional encoder representations from transformers (BERT).
In this paper, an improved training function was designed specifically for tuning of NILM neural
networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-
sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art
adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly,
we evaluated the TEAD dataset using BERT-NILM training.
Keywords:
energy disaggregation; deep learning; adaptive gradient descent optimization with
exponential long-term memory; smart grid; Internet of things; GPUs
1. Introduction
To meet the ever-growing energy demand, it is essential to monitor electricity power
consumption and moderate its usage while increasing the production capacity. Indeed,
load and energy management are essential; thus, demand-side management (DSM) with
higher potentials and better results is more common. The introduction of DSM into the
household sector can enable load management by both the user and the electric utility
company through distinguishing the loads. For instance, controlling appliances such as
cooling and heating devices with great power demand during peak hours by DSM would
enable us to supply a minimum level of energy to a larger group of users. In addition,
DSM [
1
] can help the user to understand the behavior of each device connected to the
grid, facilitating both the grid and the user to better manage their energy use. The DSM
Energies 2021, 14, 4649. https://doi.org/10.3390/en14154649 https://www.mdpi.com/journal/energies
