Synchronised Power Scheduling of Widely Distributed Refrigerators
using IoT
M. R. Zavvar Sabegh and C. M. Bingham
School of Engineering, The University of Lincoln, Brayford Pool, Lincoln, U.K.
Keywords: Internet of Things (IoT), IP-based Synchronized Wireless Mesh Network, Model Predictive Control (MPC),
Smart Home.
Abstract: The paper proposes an IoT controlled platform to remotely monitor and control appliances in the residential
sector. An IP-based synchronized wireless mesh network is implemented through IoT hardware (based on a
NodeMCU) and Google Sheets to monitor and schedule the operation of aggregated domestic refrigerators
under a Model Predictive Control (MPC) scheme. Benefits afforded by the proposed technique are
investigated through experimental trials from VonShef 13/291 (50W), iGENIX IG 3920 (55W) and Russell
Hobbs RHCLRF17B (50W) domestic refrigerators sited in three different domestic locations in the city of
Lincoln, UK. Results demonstrate the ability to monitor and control widely distributed networks of
refrigerators and adaptively schedule the appliances to reduce peak operational loads and facilitate Demand
Side Response (DSR). Further widespread expansion of the proposed technique would allow for a rapidly
deployed regional DSR strategy to aid grid stability. Ultimately the underlying principles also could be used
for the co-ordinated scheduling of other distributed appliances and equipment, both domestic and industrial.
The emergence of IoT and Thermostatically
Controlled Loads (TCLs) can be utilized to facilitate
the implementation of ancillary services and demand-
side response schemes in power systems. A key factor
for improving the management of energy
consumption in the residential sector is by the remote
real-time supervision and control of domestic
appliances. Consequently, the need for remote
aggregated scheduling of widely distributed networks
of domestic appliances is gaining increasing attention
for smart cities (Talari et al. 2017).
Recently published research shows the potential
of accessing home appliances remotely and
implementing intelligent smart home systems based
on web-based and smartphone applications (Pavithra
and Balakrishnan 2015, Vishwakarma et al. 2019, Li
et al. 2019, Mandula et al. 2015, Govindraj et al.
2017, Lokhande and Mohsin 2017, Al-Ali et al. 2017
and Xiao et al. 2018). Vishwakarma et al. 2019
propose the implementation of IoT connections for
home appliances through Google Assistant (voice
commands) as well as World Wide Web services to
switch on/off devices such as fans and lighting etc.
without requiring any physical interaction, for the
elderly or those with disabilities for instance, whilst
Mandula et al. 2015 proposes an IoT based home
automation system using an Arduino microcontroller
for indoor and outdoor remote control of appliances.
It develops an Android-based application to provide
on/off control of six home electrical appliances
namely lamp, AC, fan, refrigerator, TV and washing
machine. Furthermore Li et al. 2019 uses a
STM32F407 embedded development board for
remote environmental monitoring. It shows how the
integration of the STM32F407, Reduced Media
Independent Interface (RMII), Flexible Static
Memory Controller (FSMC) interface and web
development can provide a real-time remote monitor
function. Govindraj et al. 2017 develops an Android-
based application for a smart home automation
system using ThingSpeak for data acquisition and
visualisation, whilst Al-Ali et al. 2017 propose the
inclusion of Big Data in IoT with a unique IP address
in a mesh wireless network of devices and
recommend users to remotely monitor and control
devices and generate on-line bills via a mobile app.
Xiao et al. 2018 employ an ESP8266 and a MCU
STM32F103 microcontroller to realise a home
appliance control system with a mobile terminal for
remote control, whilst Hanumanthaiah et al. 2019
Sabegh, M. and Bingham, C.
Synchronised Power Scheduling of Widely Distributed Refrigerators using IoT.
DOI: 10.5220/0010401200890093
In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021), pages 89-93
ISBN: 978-989-758-512-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
develop a low-cost smart switch system based on an
Arduino UNO interface to facilitate remote
controllability and cloud analytics. Finally, home
automation using Message Queuing Telemetry
Transport (MQTT) and Raspberry Pi is proposed by
(Upadhyay et al. 2016) to enable measurements of
temperature and humidity. Such systems enable users
to benefit from the remote monitoring and control of
devices at isolated sites. However, more substantial
benefits can be obtained from the coordinated control
of widely distributed home appliances for aggregated
load management and load shed for Demand Side
Response (DSR) purposes. A suitably widespread
mesh network affords the opportunity to schedule and
monitor the operation of distributed domestic
appliances simultaneously to collectively improve
energy efficiency and reduce peak power
consumption on the electrical grid. Here then, the
paper proposes the implementation of a Model
Predictive Control (MPC) scheme, initially proposed
by Zavvar Sabegh and Bingham 2019, to provide the
co-ordinated scheduling of power to domestic
refrigerators of a number of households around the
City of Lincoln, UK. Measurements are taken from
the refrigerators under the IP-based wireless mesh
network and Google Sheets is used for cloud data
acquisition and monitoring. The remaining sections
of the paper are organised as follows: Section 2, a
synchronized wireless mesh network is proposed to
remotely monitor, and schedule domestic
refrigerators; Section 3 describes the experimental
setup to implement the proposed network on three
widely distributed refrigerators using a
microcontroller and Google Sheets. Section 4
presents the result of the experiments for monitoring
and scheduling modes. Section 5 concludes the work
presented in the paper.
The proposed network shown in Figure 1 consists of
two main parts: the server and client units. Client
units are widely deployed at different sites. Each
client is assigned a unique public IP address to
provide remote access and are used to collect
measurements (e.g. internal fridge temperature,
ambient temperature and the power consumption of
the fridge), send the measurements to the server via
received HTTP requests, receive control instructions
from the server and apply them to the house
appliances (e.g. fridge on/off commands). The server
can be located anywhere with Wi-Fi access and is
used to send control commands and HTTP requests to
the clients using the ‘port forwarding’ feature of the
routers to collect sensor data simultaneously (the
centralized coordinator among the clients) and send
them to the Google Sheets for data acquisition and
monitoring—see Figure 1.
Figure 1: IP-based synchronized wireless mesh network
The study uses a wireless IoT scheme based on a
synchronized wireless mesh network to remotely
monitor and schedule three domestic refrigerators
under Binary Quadratic MPC control to facilitate
DSR load-shedding events. The location of the
fridges is shown in Figure 2. The server
microcontroller is sited at the University of Lincoln,
UK. The test facility components and hardware setup
are given in Figure 2. Trials are conducted using a
SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems
VonShef 13/291 (50W), iGENIX IG 3920 (55W) and
Russell Hobbs RHCLRF17B (50W) domestic
refrigerators. It is important to note that the
RHCLRF17B uses thermoelectric cooling
technology, so no compressor is employed. Each
refrigerator is instrumented with two DS18B20
waterproof sensors to measure the internal and
ambient temperatures, a NodeMCU microcontroller
to implement the Binary Quadratic MPC algorithm
and a TP-Link Smart Wi-Fi Plug (HS110) to provide
on-off control and measure the real-time power
consumption of the refrigerators. Moreover, another
NodeMCU is located at the University of Lincoln as
a server to request the data from clients
simultaneously and send them to Google Sheets.
Figure 2: System setup (a) The location of the refrigerators
in the City of Lincoln, UK (b) hardware are fridges for
measurement and control.
Measurements are taken with a fixed sampling period
of 60 seconds to show the performance of the IP-
based synchronized wireless mesh network under
conditions of i) the refrigerators operate in isolation
without any scheduling controller to show the
monitoring feature of the proposed network, and ii)
the custom Binary Quadratic MPC algorithm is
implemented for jointly scheduling the operation of
refrigerators in DSR events.
4.1 Trial A: Refrigerators Operate in
Normal Operating Conditions
without a Scheduling Controller
(Monitoring Mode)
An initial experiment is conducted under normal
operating conditions with no co-ordinated MPC
scheduling applied. This effectively shows how the
proposed network can monitor the domestic fridges
remotely. Figure 3 shows examples of 12-hour
experiment trial intervals for each refrigerator’s
internal temperature, the ambient temperature,
individual power consumption and the total
aggregated power consumption.
4.2 Trial B: Responding to DSR Events
using Power Scheduling
Measurements now show the performance of the
synchronized wireless mesh network to investigate
how the widely distributed domestic refrigerators can
respond to a DSR events using the MPC controller
proposed by Zavvar Sabegh and Bingham 2019. The
iGENIX, VonShef and Russell Hobbs refrigerators
are loaded with 12L, 6L and 3.5L of water,
respectively, and the doors remained closed for the
duration of the trials. The prediction and control
horizon parameters used in the MPC are set to 5
samples. Desired upper and lower temperature
setpoints and minimum non-working (minimum
OFF) and working (minimum ON) times per cycle for
each refrigerator, are shown in Table 1. These are
required in order to limit the ON-OFF demand
frequency so as to not unduly stress the compressor.
Table 1: Data for iGENIX, VonShef and Russell Hobbs.
Band (ºC)
on time
off time
iGENIX 3 0.5 220 240
VonShef 2.5 0.5 260 200
Russell 8 4 380 100
Synchronised Power Scheduling of Widely Distributed Refrigerators using IoT
Figure 3: Refrigerators’ internal temperatures, ambient temperatures, power consumption and total power for trial A.
Figure 4: Refrigerators’ internal temperatures, ambient temperatures, power consumption and total power for trial B.
SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems
Results are given in Figure 4. In this scenario the
Russell Hobbs unit is given preferential access to
power during the DSR event via the weighting
matrices in the MPC controller. DSR demanded with
the total power usage of 60W occurs at t = 90 mins
and lasts for 45 minutes. As can be seen from the
measurements (Figure 4), the refrigerators are able to
respond (virtually) instantaneously to power
shedding events and the peak power consumption is
limited to 60W. Moreover, all the refrigerators
maintain their temperatures within required bounds
before the DSR event whilst the Russell Hobbs unit
remains within temperature limits due to the
additional power supply priority attributed to it by the
In this paper, the IP-based synchronized wireless
mesh network is proposed and implemented for
monitoring and co-ordinated scheduling of widely
distributed domestic refrigerators, and contribute to
DSR events using simple ‘port forwarding’ of
domestic routers. The network uses Google Sheets for
data acquisition and monitoring in the cloud. The
proposed methodology readily lends itself to other
house appliances, for instance, heating ventilation
and air conditioning (HVAC) systems, or other TCLs.
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Synchronised Power Scheduling of Widely Distributed Refrigerators using IoT