Raad Z. Homod

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Artificial Neural Network Based Controller for
Home Energy Management Considering
Demand Response Events
Conference Paper · November 2016
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Raad Z. Homod
Basrah University for Oil and Gas
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2. Artificial Neural Network Based Controller for Home
Energy Management Considering Demand Response
Events
Maytham S. Ahmed ¹, a,b
, Azah Mohamed ², a
,
a
Dept. of Electrical, Electronic and Systems Engineering,
Faculty of Engineering and Built Environment, Universiti
Kebangsaan Malaysia, 43600 Bnagi, Selangor, Malaysia
b
General Directorate of Electrical Energy Production-
Basrah, Ministry of electricity, Iraq;
¹ eng_maitham@yahoo.com, 2
azah@eng.ukm.my,
Hussain Shareef 3,c
, Raad Z. Homod 4,d
, Jamal Abd
Ali5,a
c
Dept. of Electrical Engineering, College of Engineering,
United Arab Emirates University,155511 Al-Ain,UAE
d
Dept. of Petroleum and Gas Engineering, Basrah
University, Qarmat Ali Campus, 61004 Basrah, Iraq
3
raadahmood@yahoo.com, 4
hussain_ln@yahoo.com
Abstract— Electricity demand response and residential load
modeling play important roles in the development of home
energy management system. Accurate load models are required
to produce a load profile at residential level. In this paper,
modeling of four load types that include air conditioner, electric
water heater, washing machine, and refrigerator are developed
considering customer lifestyle and priority by using Matlab/
Simulink. In addition, the home energy management controller is
proposed using artificial neural network (ANN) to predict the
optimal ON/OFF status of the home appliances. The feed-
forward neural network type and Levenberg–Marquardt (LM)
training algorithm are chosen for training the ANN in the Matlab
toolbox. Results showed that the proposed ANN based controller
can decrease the energy consumption for home appliances at
specific time and can maintain the total household power
consumption below its demand limit without affecting customer
lifestyles.
Keywords— home energy management system; artificial neural
network (ANN); load scheduling; residential demand response;
energy efficiency; home appliance
I. INTRODUCTION
In recent years the concept of smart grid has been
presented as the best solution that aims to provide flexibility to
the electricity grid by using communication technologies
which enable participation from residential customers [1].
The residential sector consumed around 30–40% of the total
energy consumption all over the world [2]. Smart home is one
of the applications of smart technologies in residential
buildings that can provide opportunities for improved energy
management ,energy saving, reduced energy consumption,
reduced greenhouse gas emissions, and improved home
automation [3]. Towards enabling smart technologies at the
household level, a home energy management system (HEMS)
plays an important role for efficient operation of home
appliances coordinated by load management under residential
demand response (DR) strategies [4]. DR is one of the most
cost effective and reliable techniques used by utilities for load
shifting and scheduling. Through DR program, power utilities
and homeowners can get information about dynamic price
signals and home load profiles by using smart meters [5]. The
perspective of end-user's DR is important for allowing
integrated management of resources and for reducing power
consumption by cutting the electricity load. From the power
system point of view, DR can allow increased reliability,
improved management of the grid, decreased effects of
electric vehicle charging and enhanced load factor [6].
Participant customers in DR programs are able to predict
savings in electricity bills when they reduce their electricity
usages during peak periods and shifting peak time load to off-
peak time.
HEMS can assist to reduce overall energy consumption by
optimal residential load scheduling of appliances and allowing
achieving various goals and functions inside the homes such
as automatic control, shifting or curtailing the demand
consumption [7]. In order to cover customers limitations and
preferences and as well as to get the benefits of reduced power
consumptions of all such electrical aspects, an efficient,
HEMS controller is required [8]. Different control methods
and optimization techniques have been used to help end users
to create optimal appliance scheduling of energy usage based
on different feed-in tariffs, pricing schemes and comfort
settings. In [9], an optimization strategy is developed for
minimizing tariff for end-users with effective operation of
home appliances under different price based DR signals. In
[10], optimal scheduling of home appliances using game
theory has been presented by considering electric vehicle and
battery storage to reduce power consumption in the home. The
authors in [11] presented mixed integer nonlinear
programming model for energy saving and comfort lifestyle.
In [12], rule-based algorithm is developed for HEM to shift
and schedule home appliances with reduced energy
consumption considering residential DR application.
This paper presents application of artificial neural network
(ANN) for home energy management controller (HEMC)
considering DR events. Four residential loads are simulated in
Matlab/Simulink that includes air conditioner (AC), electric
water heater (EWH), refrigerator (REF), and washing machine
(WM). This study focuses on modeling of the above-
mentioned high power consumption appliances and the
development of ANN based HEMC to alleviate energy

3. consumption based on scheduled operation of several
appliances at specific time according to customer lifestyle and
priority of devices.
II. RESIDENTIAL LOAD MODELS
Residential load model plays an important role in studying
end user behaviors and to evaluate residential DR at the
distribution circuit. Thus, there is need to design specific home
appliance models describing the dynamics of the process to be
controlled. To develop a HEMC to manage the operation of
household appliances with DR events, it is essential to have
accurate models that can simulate the behavior of electric
loads. For the purpose of this study, four load types namely
AC, EWH, REF and WM as shown in Figure 1 have been
developed using Matlab/ Simulink.
Fig.1. HEMS controller architecture
Table.1 Domestic loads characteristics
Appliances Load
priority
Preferences Rated
Power(kW)
Water heater
(EWH)
1 Water temp.
(23-25)℃
3
Air conditioner
(AC)
2 Room temp.
(44-50)℃
2.3
washing machine
(WM)
3 1 time/day,56
minutes
0.6
Refrigerator (REF) 4 24 hours 0.15
From Figure 1, the DR signal is assumed to come from the
utility to the smart meter and then to HEMC. All residential
loads are able to receive the DR signal from the controller.
The load preferences and characteristics of EWH, AC, WM,
and REF are given in the Table 1.
A. Air conditioning model
The parameters of AC unit can be divided to two
categories; the characteristics of AC and building structures.
The input parameters of load model are the room temperature,
, at time t, outside temperature, , , occupant heat gain,
, set point temperature, , and DR signal, , . The
outputs of the residential model are power consumption and
room temperature that are used as inputs to the model at the
next step of time [12, 13]. If the temperature inside a room is
lower than its allowable set point temperature, the AC will
turn OFF. If the temperature of a room reaches its maximum
set point, the AC will turn ON. However, the AC keeps the
same status, when the room temperature is within its setting as
described mathematically in Eq. (1)
=
1, , > ( , + ∆ ) ∗ ,
0, , < ( , − ∆ ) ∗ ,
, , − ∆ ≤ , ≤ , + ∆
(1)
where is the status of device, device is turned on
when ,=1 , the device is turned off when ,= 0.
The power consumed in kW with a thermostat operating in
OFF or ON mode and running at its rated power when
switched on at a given interval, can be expressed as:
, = ∗ (2)
where is the rated power in kW. By using the Eq.(1)
and (2) the model can be simulated.
B. Water heater model
To develop an accurate EWH model, the input and output
parameters are first defined. The output of the model is the
room temperature that is used as input to the model at the next
step of time and energy consumption [12, 13]. The input
parameters are the ambient temperature, , set point
temperature, , , water flow rate, , temperature of inlet
water, , signal of residential DR, , and temperature
of water tank, , . The operation of the EWH depends on
the status of the device, which is expressed
mathematically as,
=
1, , < ( , − ∆ ) ∗ ,
0, , > ( , + ∆ ) ∗ ,
, , − ∆ ≤ , ≤ , + ∆
(3)
where , is the DR signal, , is the set point
temperature, ∆ is the dead band temperature, ±2℃.
The amount of EWH power consumed in kW depends on the
thermostat operating in the ON or OFF. The power of EWH at
a given time is calculated by using,
, = ∗ (4)
where is the status of device, =1 device is
switched on, = 0 mean the device is switched off
and is the EWH rated power in kW. By using Eq. (3) and
(4) the EWH is simulated.
C. Washing machine and refrigerator modelling
Real data were measured by using a power quality
analyzer to model the WM and REF. Then a Matlab/Simulink
is developed using resistors and reactances. The rated power
of the WM depends on the stage of washing cycles including
washing, rinsing and spin to finish the WM job at full load
with 54 litters of water needed and duration of typically 56

4. minutes. Each cycle of washing, rinsing and spinning have
different power consumption.
The REF power consumption was measured with the doors
closed and the temperature settings on the REF and freezer
were 2℃ and -17℃, respectively.
III. APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR
HEMC
An ANN is information processing algorithm which
models non-linear systems and tries to simulate the human
brain. It has a unique capability especially with the complex
nonlinear relationships between system input and output
which tackle any complex nonlinear functions through
training. In this paper, a feed-forward neural network type and
Levenberg–Marquardt (LM) training algorithm are chosen for
training the ANN in the Matlab toolbox as shown in the Figure
2.
Fig.2. Architecture of the ANN
The ANN structure consists of two hidden layers, four
inputs ( , , , ), and four outputs (AC, EWH, WM,
REF). The actual training data for the ANN are generated
from the simulation models. From Figure 2, the inputs are the
room temperature, is in ℃, temperature of electrical water
heater, , demand response signal, and total power
consumption, . On the other hand, the outputs of the ANN
are the signals to turn ON or OFF the four home appliances
that include AC, EWH, WM and REF according to the
customer lifestyle and priority of appliances. The ANN
parameters are shown in Table 2. The sudden changes of the
home appliances can be predicted by using ANN. The
regression coefficient (R) for the ANN training is 0.99518 and
it is close to 1 as shown in Figure 3.
The ANN parameter that include the Number of inputs,
Number of outputs, Number of hidden layer, Number of
hidden layer, Number of neurons in hidden layer (N1,N2), and
the Learning rate with Number of iterations are shown in
Table 2.
Table.2 ANN parameters
Parameters Value
Number of inputs 4
Number of outputs 4
Number of hidden layer 2
Number of neurons in hidden layer N1 18
Number of neurons in hidden layer N2 20
Number of iterations 1000
Learning rate 0.6175
Fig.3. Performance of ANN training
IV. SIMULATION RESULTS
Two case studies are considered to evaluate the
performance of the ANN based HEMC in terms of reducing
the power consumption and saving the energy in the home.
The first case is without HEMC and the second case assumes
that the utility sends signal to the smart meter installed in the
home to reduce power consumption with demand limit of 4kW
at imposed time between 17 to 22 hours as shown in Figure 4.
From Figure 4, with demand limit of 4kW, the AC, WM
and REF have to be shut OFF and only keep the water heater
switch ON so as to keep the power consumption lower than
the demand limit considering the priority of appliances as in
table 1. The other appliances require shifting their schedule
after the demand limit period. The results verify the
performance of the proposed HEMC in keeping the total
power consumption of the four residential loads at a specific
time below the demand limit value by using the ANN based
HEMC. The energy saving for the total power consumption is
3.0832 % per 5 h without any effect on the customer lifestyle.
The HEMC does not allow the total power consumption
exceed the selected value of demand limit.
0 0.2 0.4 0.6 0.8 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Target
Output~=0.99*Target+0.0084
Training: R=0.99518
Data
Fit
Y = T

5. V. CONCLUSION
This paper presented the proposed ANN based HEMC
using the feed-forward neural network type and the
Levenberg–Marquardt algorithm for training the ANN. For the
home energy management controller, four residential loads,
namely, AC, EWH, WM, and REF were simulated according
to the physical and operation characteristics to manage the
power consumption and reduce the electricity bills by
considering the DR signal. The proposed ANN controller
gives good response in switching the AC, EWH, WM, and
REF such that the loads operate within the demand limit value.
By using the controller, the energy saving for the total power
3.0832 % per 5 h and it prevents the total power consumption
from overriding the selected value of demand limit.
ACKNOWLEDGEMENT
The authors greatly acknowledge University Kebangsaan
Malaysia for funding this project under DIP-2014-028.
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Fig.4.Total power consumption before and after DR signal with the ANN based HEMC
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
2
4
6
8
10
Time in hour
Power(kW)
Without HEMC With HEMC
Water heater
Demand Resopnse period
Refrigerator Air condition
Washing
machine
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