Citation: Philip, A.; Islam, S.N.;
Phillips, N.; Anwar, A. Optimum
Energy Management for Air
Conditioners in IoT-Enabled Smart
Home. Sensors 2022, 22, 7102.
https://doi.org/10.3390/
s22197102
Academic Editors: Fabio Salice and
Sara Comai
Received: 1 August 2022
Accepted: 13 September 2022
Published: 20 September 2022
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Article
Optimum Energy Management for Air Conditioners in
IoT-Enabled Smart Home
Ashleigh Philip
1
, Shama Naz Islam
1
, Nicholas Phillips
2
and Adnan Anwar
3,
*
1
Deakin University, Waurn Ponds, VIC 3216, Australia
2
Itron Australasia, Melbourne, VIC 3000, Australia
3
Strategic Centre for Cyber Security Research and Innovation (CSRI), School of Information Technology,
Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
* Correspondence: adnan.anwar@deakin.edu.au
Abstract:
This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in
Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can
be achieved. The proposed strategy utilises renewable energy generation periods and moves some
of the air conditioning loads to these periods to reduce the electricity demand. In particular, we
propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage
to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to
low price periods at the second stage. The proposed approach is investigated for the temperature
and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the
approach developed can significantly reduce the energy consumption and cost associated with AC
operation for nearly all days in summer when cooling is required. Specifically, the proposed approach
was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest
average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme
performed better when the thermal insulation levels in the smart home are higher. However, the
optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation
conditions compared to the no pre-cooling case.
Keywords:
energy management; pre-cooling; air conditioners; IoT-enabled smart home; time of use
1. Introduction
There is currently a global energy crisis, as energy consumption across the world is
increasing at a significant rate every year. Residential consumers contribute significantly to
this total global energy consumption, to seasonal and daily peak demands, and account for
a total of 30% to 40% of energy consumption around the world [
1
]. A significant proportion
of residential energy consumption is attributed to heating, cooling, ventilation and air
conditioning (HVAC) systems. For example, in Finland, HVAC loads from the residential
sector account for 70% of total annual energy consumption [
2
]. On the other hand, 40% of
the residential energy consumption in Australia comes from space conditioning [
3
]. As
renewable generation, smart-grid technologies and smart meters are being added into the
network, there is a renewed interest in integrating demand-side management strategies into
the network with decreasing technological barriers [
4
,
5
]. The advancement of Internet of
Things (IoT) technologies has allowed the integration of intelligent algorithms for demand
management applications in smart homes. Residential demand management tools, such
as home energy management systems (HEMS), can assist in significantly reducing peak
load demand by integrating optimum operational strategies for energy consumption in
smart homes [
6
]. Thus, effective demand management schemes need to be integrated in
residential HEMSs to reduce the energy consumption of HVAC systems [7].
Existing demand response initiatives rely on incentive-based programs where partici-
pants are offered participation credits, or credits based on performance. These programs
Sensors 2022, 22, 7102. https://doi.org/10.3390/s22197102 https://www.mdpi.com/journal/sensors
