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SPECIAL SECTION ON INTELLIGENT SYSTEMS

FOR THE INTERNET OF THINGS

Received September 12, 2017, accepted October 4, 2017, date of publication October 17, 2017, date of current version March 7, 2019.

Digital Object Identifier 10.1109/ACCESS.2017.2763624

Towards Optimization of Metaheuristic

Algorithms for IoT Enabled Smart

Homes Targeting Balanced Demand

and Supply of Energy

SAQIB KAZMI1, NADEEM JAVAID 1, (Senior Member, IEEE), MUHAMMAD JUNAID MUGHAL1,

MARIAM AKBAR1, (Member, IEEE), SYED HASSAN AHMED 2, Member, IEEE),

AND NABIL ALRAJEH3

1COMSATS Institute of Information Technology, Islamabad 44000, Pakistan

2School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, South Korea

3College of Applied Medical Sciences, King Saud University, Riyadh 11633, Saudi Arabia

Corresponding author: Nadeem Javaid (nadeemjavaidqau@gmail.com)

This work was supported by the Deanship of Scientiﬁc Research, King Saud University, under Grant RG-1435-0037.

ABSTRACT Internet of Things enabled smart grid (SG) is one of the most advanced technologies,

which plays a key role in maintaining a balance between demand and supply by implementing demand

response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM)

system, which is called demand side management. Appliance scheduling is an integral part of HEM system as

it manages energy demand according to supply, by automatically controlling the appliances and shifting the

load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded

with heuristic algorithms, such as harmony search algorithm, enhanced differential evolution, and harmony

search differential evolution, is evaluated. The integration of renewable energy source (RES) in SG makes

the performance of HEM system more efﬁcient. The electricity consumption in peak hours usually creates

peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances

in the peak hours. We formulate our problem using multiple knapsack theory that the maximum capacity of

the consumer of electricity must be in the range, which is bearable for consumer with respect to electricity

bill. Feasible regions are computed to validate the formulated problem. Finally, simulation of the proposed

techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms

of cost, peak-to-average ratio, and waiting time minimization.

INDEX TERMS Smart grid, knapsack, enhanced differential evolution, harmony search algorithm, home

energy management system, demand side management.

NOMENCLATURE

Variables and Subscripts Description

tTime interval

UEnergy consumed by

ﬁxed appliances

VEnergy consumed by ﬂexible

appliances

WEnergy consumed by

un-interruptible appliances

ρPower ratings of appliances

Fap Set of ﬁxed appliances

Sap Set of shift-able. appliances

UIap Set of un-interruptible appliances

γON/OFF status of appliances

P(t) Total power consumption of shift-able., ﬁxed and

un-interruptible appliances

C(t) Total electricity bill

λPricing signal (RTP)

EPV Energy generated from RES

ηEfﬁciency of solar inverter

IrSolar irradiance

APV Area of solar panel

VOLUME 7, 2019

2169-3536 2017 IEEE. Translations and content mining are permitted for academic research only.

Personal use is also permitted, but republication/redistribution requires IEEE permission.

See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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S. Kazmi et al.: Toward Optimization of Metaheuristic Algorithms for IoT Enabled Smart Homes

TaOutdoor temperature

CMaximum allowed power consumption

xij Initial population

xlLowest value in population matrix

xuHighest value in population matrix

UjTrial vector

VjMutant vector

FMutant factor

CR Crossover rate

xrl Target vector of EDE

τsch Operation start time of appliances

τot Operation end time of appliances

Hij Harmony memory

HlLowest value in HM

HuUpper limit of values in HM

x0

iNew harmony vector

Vr1Target vector of HSDE

Hji Trial vector of HSDE

NI Maximum no. of iteration

I. INTRODUCTION

Energy demand around the world is increasing day by day.

To fulﬁl this demand the existing generation is facing a lot of

challenges. It is estimated that total energy demand at the end

of 2020 will increase by 75% as compared to energy demand

in 2000 [1]. This increase may force utilities to reshape

electricity generation and distribution in order to avoid

demanding energy challenges. In order to fulﬁl energy

demand, different means of electricity generation like renew-

able and sustainable energy resources (RSER) are introduced

in the power system. The integration of renewable energy

sources (RESs) in the existing traditional grid increases

system complexities and dynamics [2]. Smart grid (SG)

is another advancement in the ﬁeld of power system.

In SG, advanced information and communication technolo-

gies (ICTs) are introduced in traditional grids which provide

customers the ability to interact with utility [3]. Advanced

metering infrastructure (AMI) equips each home with smart

meter that gathers energy demand information from cus-

tomer and uploads to the utility server. SG allows inte-

gration of renewable and distributed energy generation to

diminish the effects of CO2on environment and to optimize

energy consumption. On the other hand demand side manage-

ment (DSM) is a very important aspect of SG that efﬁciently

manages the energy demand of end users by enabling the

exchange of information between utility and consumers. The

EDE parameters are summarized in Table 2. These programs

aim at improving grids stability by reducing peak forma-

tion [4]. So, utilities and customers can manage the energy

generation and consumption through the implementation of

DSM programs by providing incentives or encouraging the

customers to participate in energy management programs.

The two-way ﬂow of information and energy in SG keeps the

electricity consumers informed about the pricing rates, load

on utility, load shedding schedules and any type of equipment

failure due to any natural or crew cause. It enables the utility

company to monitor and analyze the real time information of

consumers so that responsive actions may be taken according

to utility/end-users demand. End users can take economic

beneﬁts by shifting peak load to off-peak hours using differ-

ent optimization techniques.

In a traditional power system, utility companies manually

shed the selected load of consumers during peak hours to

make their operations safe. On the other hand, load shifting

in SG is accomplished in peak hours to avoid peak forma-

tion. This not only beneﬁts the users but also the utility

company. This strategy in turn increases the reliability of

the grid. Although shifting the load from high peak to low

peak hours reduces the peak load and electricity cost, but

it disturbs user comfort. So, there is a trade-off between

user comfort and cost saving, which cannot be achieved at

the same time. Thus, peak to average ratio (PAR), energy

price signals, daily energy consumption and user comfort are

the constraints needed to be considered. Beside considering

end users desires and needs, utility companies also provide

incentives to motivate consumers to reschedule their load to

mitigate high peaks. Such challenges have motivated the need

for intelligent energy management algorithms that can handle

all types of loads and responds to price variations.

To address the aforementioned challenges, this paper

presents energy management algorithms to schedule

household appliances while meeting the constraints. The

algorithms used in this paper are harmony search algo-

rithm (HSA), enhanced differential evolution (EDE)

algorithm and our proposed hybrid technique; harmony

search differential evolution (HSDE) algorithm. The behavior

of the system embedded with these algorithms is smart

and user satisfactory with respect to billing, PAR, energy

consumption and the waiting time.

The rest of the paper is organized as follows: Section II

discusses the related work. Section III elaborates pro-

posed system model and problem formulation is provided

in Section IV. Proposed schemes are discussed in Section V.

Feasible regions are brieﬂy discussed in Section VI.

In Section VII performance evaluation through simulation

of proposed algorithms is elaborated in detail. Section VIII

concludes this paper.

II. LITERATURE REVIEW

Researchers around the world work to optimally schedule

the appliances to beneﬁt the consumers. Most of the electric

utility companies are investigating and implementing SG to

make the existing power system advanced, reliable, self-

healing and economical. The use of sensors, communication

and computational ability and controlling characteristics in

SG enhance the overall operation of electric power transmis-

sion system [5]. PAR, daily energy consumption, electricity

cost and the hourly energy consumption of shift-able. and

throttle-able appliances of the consumers are the constraints

and the objective function in [6], all of which are to be mini-

mized. This is accomplished by shifting the high energy con-

suming loads to off peak hours which helps to minimize the

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energy consumption in the peak hours. The authors formulate

the initial optimization problem, to minimize the energy cost

of the consumers by determining of the optimal usage of

the power and operational time of throttle-able and shift-

able. appliances. The authors use the distributed algorithms,

which ﬁnd the near-optimal schedule with minimal infor-

mation exchange between the residential scheduler and con-

sumers. The smart appliances present in smart homes work

in automated fashion that provide consumers high comfort

at less expense. In order to make home energy consumption

more efﬁcient and to control the power supply and demand

numerous researches are presented.

Khosla [7] present a review of different research works

on a wide range of energy management controllers for smart

homes which reduce energy consumption, PAR and energy

wastage. Various HEM schemes, pricing schemes, such as

real time pricing (RTP), critical peak pricing (CPP), time of

use (TOU) and day ahead pricing (DAP) and energy con-

sumption are discussed in detail. Bao et al. [8] propose a

hybrid approach (WDO-DE) from two existing techniques,

i.e., wind driven optimization (WDO) and differential evo-

lution (DE) algorithms. Fifteen benchmark functions are

tested which contain unimodal, multi-modal, low dimen-

sional and high dimensional to check the performance of

proposed algorithm. The experimental results show that the

proposed algorithm can be feasible in both low-dimensional

and high-dimensional cases. The simulation results show that

the performance of WDO-DE algorithm is better than genetic

algorithm (GA), binary particle swarm optimization (BPSO),

WDO, DE, and PSO to reach the optimal solution. The appli-

cation of heuristic algorithms is not conﬁned to scheduling

of energy consumption of appliances, these algorithms are

used in vast number of applications in many other ﬁelds such

as image segmentation, cloud resource allocation, multi-level

thresholding etc. as in [9] the authors propose a new quan-

tum wind driven optimization (QWDO) for path planning

of unnamed combat air vehicle (UCAV) in the battleﬁeld

considering the different threats and constraints. Two test

instances are chosen in order to evaluate the performance

of the proposed algorithm. The experimental results depict

that the QWDO algorithm is an appropriate and reliable

technique to solve the UCAV path planning problem and

when compared with other algorithms it shows a better search

performance. The combined pricing schemes of TOU and

inclined block rate (IBR) is used for bill calculation in [10].

In this paper, the authors present an efﬁcient DSM model for

residential energy management system in order to avoid peak

formation while decreasing electricity bill and preserving

user comfort level within acceptable limit. For this purpose,

three heuristic algorithms (GA, BPSO, and ACO) are used

to evaluate the objective function. They suggest that the

GA based EMC is better in term of electricity bill reduction

and PAR minimization and maximization of user satisfaction

than BPSO and ACO. However the computational time of

the algorithm is higher. Arafa et al. [11] reduce the com-

putational time by introducing evolutionary algorithm in an

enhanced way, that improves the performance (convergence

rate and accuracy) of DE called EDE for load scheduling

in homes. It has less number of parameters to control. The

algorithm is tested on 47 benchmark functions. All the steps

are followed as in DE only the number of trial vectors is

increased to minimize the chance of repetition of selection

reduces. Many research works are still in progress to improve

the performance of the algorithm in order to make them more

compatible with increasing demands.

Moon and Lee [12] study a society based load scheduling

problem with different classes of appliances in the grid. While

designing the optimization algorithm the overall society0s

satisfaction is kept under consideration. They design the

smart grid with various constraints such as minimize energy

consumption, alter peak formation and limit the budget. The

overall society electricity usage pattern is observed keenly

and sum of net consumption of all homes and their electric-

ity cost are compared which give near-optimal scheduling.

On the base of collected data the lower and upper bounds of

the objective function are formulated. The simulation results

show that the algorithm is effective in treating heterogeneous

residences.

In [13], the negligence of user comfort in the previous

papers is given consideration along with electricity cost sav-

ing and PAR reduction. They propose GA based algorithm

for DSM. Five types of appliances are taken for scheduling

and their mathematical models are formulated by considering

thermal and comfort constraints. The pricing scheme used in

this paper is RTP. When there are peak hours they integrate

micro grid with the traditional grid so that the bill can be

reduced and also user comfort can be increased.

The algorithms enable the consumers to pursue the best

consumption beneﬁts within consumption range. To improve

the ﬁnancial beneﬁts of the electricity consumers, a novel

concept of cost efﬁcient load scheduling framework is intro-

duced in [14]. The authors merge the two pricing techniques

RTP and DAP by using fractional programming approach.

They have explained the effect of simple power shifting of

speciﬁc appliances on the consumption cost, to show the

direct relationship between consumption load pattern and

cost. Further proposed algorithm allows the consumers to

fully utilize the electricity with remarkable savings of bill.

Another contribution of the paper for the cost saving and

minimization of CO2emission in environment is integration

of RES into the grid. This RES along with the fractional

programming reduce the electricity cost efﬁciently. How-

ever, the PAR minimization and user comfort are not dis-

cussed in the paper. Ahmad et al. [15] propose an optimized

HEM system (OHEMS) to minimize the electricity bill in

response to dynamic pricing by scheduling the household

appliances using four heuristic algorithms; GA, BPSO, WDO

and bacteria foraging optimization (BFO) algorithm. More-

over they offer a new hybrid GA-PSO (HGPO) algorithm

which incorporates the positive features of GA and PSO

algorithms in a single algorithm. RES and ESS are integrated

to encourage the consumers to take part in DSM.

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TABLE 1. Categories and parameters of appliances.

A new meta-heuristic, population based algorithm mimick-

ing the improvisation process by musicians was developed

by Geem et al. [16] in 2001. It is simple concept based,

less parameterized and easily implementable algorithm. It has

been applied to various engineering and non engineering opti-

mization problems successfully. Unlike GA, HSA generates

a new optimal vectors considering all the available vectors in

the search space, whereas, in GA only two parent vectors are

selected to produce one better offspring. Therefore the ﬂex-

ibility and accuracy of HSA is better than other algorithms.

We have further enhanced its performance by hybridizing it

with EDE in this paper.

The ﬁxed value of pitch adjustment ratio (PAR) and arbi-

trary distance bandwidth (bw) in improvisation step of the

traditional HSA cause poor performance and increase the

number of iterations needed to ﬁnd the optimum solution.

Mahdavi et al. [17] introduce a variable PAdR and bw

values in the improvisation step to overcome the draw-

back. Moreover to check the effectiveness of improved

HS, they apply this to different constrained and uncon-

strained benchmark functions. In [18], unit commitment

problem is solved using HSA. The total production cost

is minimized by optimizing the controllable parameters

within the limits. The HSA has fast convergence time

and is economical than conventional and improved GA.

Fesanghary and Ardehali [19] present a novel meta-heuristic

approach based on HSA to solve economic dispatch (ED)

problem to minimize the total power generation cost. They

formulate two approaches; swarm intelligence concept and

hybrid harmony search quadratic programming (HSQP)

to improve the quality and convergence rate of HSA.

Karthigeyan et al. [20], the authors compare the performance

of HS, bio-geography based optimization (BBO) algorithm

and improved harmony search (IHS) algorithms for solv-

ing constraint economic dispatch power in power system.

Twenty generating units are tested through these algorithms

with ramp rate limits and valve point loading constraint. The

improved HSA gives minimum fuel cost and good conver-

gence characteristics as compared to HSA and BBO. The

integration of RES in SG, its economical beneﬁts in term of

cost reduction and challenges while integration is discussed

in [21]. Different types of RESs are available for integration

FIGURE 1. Proposed system model.

with SG in market nowadays. Phuangpornpitak and Tia [22]

discuss in detail, the various sources of renewable energy,

their utilization and trends of technological advancement in

RES integration with SG. The security issues and impact of

RES on environment are also elaborated.

III. PROPOSED SYSTEM MODEL

DSM in a smart grid makes operation of the grid more reliable

and stable. In smart home it manages and controls the energy

usage by scheduling the appliances according to the scheduler

embedded in the HEM system [23]. The smart meter allows

two way ﬂow of information between consumer and utility,

i.e., pricing signal and load demand. The information is sent

to the EMC by smart meter and EMC accordingly schedules

the appliances in the smart home based on pricing signal,

load demand and user preferences. Simple architecture of

HEM system is shown in Fig. 1.

A. LOAD CATEGORIZATION

We classify appliances into three categories; ﬁxed, shift-

able. and uninterruptible appliances according to consumer

usage behavior. The power rating of appliances and time of

operation in a day is summarized in Table 1. Details of these

categories are given below.

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1) FIXED APPLIANCES

These are called ﬁxed appliances because their operation

length cannot be modiﬁed. The scheduler has to sched-

ule these appliances between user deﬁned time-slots. These

appliances are light, AC, refrigerator and oven. We represent

set of ﬁxed appliances as Fap and its power consumption as U.

faFap has power rating of ρfa, then total power consumed

by ﬁxed appliances in each timeslot is calculated using the

following equation,

U(t)=

T

X

t=1X

faFap

ρfa×γfa(t),(1)

where, a= {1,2,...,n},T=24 for one day, γfa(t) is

ON /OFF state of the appliance in the respective time-slot.

2) SHIFT-ABLE APPLIANCES

These appliances are those which can be shifted to any time-

slot and when required can be interrupted during operation.

These appliances include vacuum cleaner, water pump, water

heater, and fans. As the AC and fans are both used for cool-

ing purpose, the scheduler will schedule them accordingly

so that user can get maximum comfort. We represent shift-

able. appliances as Sap and its power consumption by V.

saSap has power rating of ρsa, then the total power consumed

by shift-able. appliances in each time-slot is calculated in the

following equation,

V(t)=

T

X

t=1X

saSap

ρsa×γsa(t),(2)

where, γsa(t) is the ON /OFF state of the appliance in that

hour. Our focus is to minimize the per hour cost of each

appliance, as a result, the overall cost will be reduced.

3) UN-INTERRUPTIBLE APPLIANCES

These appliances can be delayed or schedule earlier but once

started operation cannot be interrupted until the operation

completes. Washing machine, cloth dryer and dishwasher are

included in this category. UIap is the set of un-interruptible

appliances such that uiaUIap and ρuiais the power rating

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of each appliance. The total power consumption Wof this

category appliances can be calculated as,

W(t)=

T

X

t=1X

uiaUIap

ρuia×γuia(t).(3)

B. INTEGRATION OF RES

The roof of smart home is ﬁtted with rooftop photo-

voltaic (PV) energy source. The smart home is not fulﬁlled

whole of its electricity demand by the PV source rather only

when the utility is providing electricity with peak pricing, the

PV source operates. The EMC integrates the PV source with

the utility grid when required. In each time-slot the power

generated by PV source is EPV (t).

The PV source provides the energy for the time-slots

tα−tβ,so the total energy generated each day by the

PV source can be calculated as,

EPV =

tβ

X

t=tα

EPV (t),(4)

The PV source can be integrated only if it is meeting a

minimum capacity deﬁned below,

EPV ≥Emin

PV .(5)

In the smart home forecasting device of the expected

PV generation is installed according to which the scheduler

schedules the appliances. The forecasted output power of

PV source is affected by many factors like solar irradiance Ir,

area of the solar panel APV , the outdoor temperature at that

time Ta(t) and inverter efﬁciency η. The generated output

power of the PV source can be calculated by, [24].

EPV =ηPV ×APV ×Ir(1 −0.005(Ta(t)−25)),(6)

C. ENERGY CONSUMPTION MODEL

The total energy consumption of all the appliances in each

hour can be calculated using the following equations,

PT(t)=W(t)+V(t)+U(t),(7)

PT(t)=X

faFap 24

X

t=1

ρfa×γfa(t),

+X

saSap 24

X

t=1

ρsa×γsa(t),

+X

uiaUIap 24

X

t=1

ρuia×γuia(t).(8)

To calculate the total energy consumed (demand of con-

sumer) in a day, the per hour energy consumption is calcu-

lated and added.

D. ENERGY COST MODEL

The electricity cost is calculated by multiplying pricing signal

with energy consumed by appliances.

CT=

T

X

t=1

(P(t)×λ(t)),(9)

where, λis pricing signal used in our work. We have taken

RTP scheme which has per hour changing behavior w.r.t price

and remains unchanged in that hour. The price or rate at which

electrical energy is supplied to consumers is called tariff.

Numerous electrical tariffs are available to deﬁne the energy

pricing over a day such as ToU, DAP, RTP, CPP etc.

IV. PROBLEM FORMULATION

Formulation of objective function is a key step in optimization

problem. In this paper, the objective function is deﬁned as

electricity consumption cost minimization to achieve maxi-

mum user comfort. The smart home is equipped with smart

meter which sends the consumer’s energy demand and pref-

erences to the utility company. The utility accordingly offers

DR signal which contain necessary load scheduling and

optimization. The smart meter can directly communicate

with EMC and grid. The EMC deﬁnes operating sched-

ule of household appliances and communicates with appli-

ances using communication technologies. Home appliances

are categorized based on operating time and energy con-

sumption requirement for efﬁcient management of energy.

We formulate our problem as knapsack that the total elec-

tricity consumption must not exceed the maximum capacity

deﬁned, i.e.,

max Xpi×xi,(10)

s.t.,

n

X

i=1

wi×xi≤C,(11)

where, pishows the proﬁt of each item, wirepresents weight

of each item and xirepresents the binary number 1 and 0

means ON/OFF state of each appliance. (11) shows that the

collective weight after considering maximum proﬁt must not

exceed the capacity C of the knapsack. We consider appli-

ances as items and weight as power ratings of appliances.

The operational cost of an appliance is taken as its proﬁt.

Our objective is to minimize the electricity cost by getting

maximum proﬁt,

min

T

X

t=1

m

X

i=1

PT×γi(t),(12)

s.t.,

T

X

t=1

m

X

i=1

PT×γi(t)×λ(t)<C,(13)

and,

τsch =τot ,(14)

where, γi(t) is the ON/OFF state of the appliance at the time-

slot t,τsch,τot represent the operation start and ending times

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TABLE 2. Parameters of EDE.

of the appliance and Cis the maximum allowable power to

be consumed. Violation of this constraint may lead to peak

formation and system stability issues. For this purpose, the

scheduling algorithms must follow the knapsack constraint.

When the amount of consumed power exceeds the maximum

limit, the EMC computes new optimal allocation using an

algorithm and sends control message to the appliances. Once

the power consumption constraint is satisﬁed the scheduler

gives a consumption pattern, now accordingly we formulate

our energy consumption cost.

V. PROPOSED SCHEMES

The appliance scheduling problem formulated in section IV

is evaluated using three heuristic algorithms (HSA, EDE

and our proposed technique HSDE). Contrary to classical

optimization techniques like linear programming (LP) [25],

integer linear programming (ILP) [26], and mixed integer

linear programming (MILP) [27], heuristic algorithms poses

fast convergence rate and simple steps to reach the optimum

solution. The proposed algorithms are discussed in detail

below.

A. EDE

The EDE algorithm is advanced form of differential evolu-

tion algorithm introduced by Storn and Price [28] in 1995.

It has now become one of the most common technique to

solve the scheduling problem. This algorithm has only three

parameters; mutation, crossover, and selection. The tuning

control parameters are size of the population, scaling factor

of mutation, and crossover rate. Like all other algorithms the

ﬁrst step is the random population generation. The generation

of population in EDE algorithm is simple given as,

xij =xl+rand(1) ×(xu−xl),(15)

where, xij is the initial population and xland xuare lower and

upper limits of the values in the population. This population

is in the form of real numbers between the upper and lower

limits which is given in the parameters deﬁned. Once the

population is generated it is converted to binary number by

any function like sigmoid function. The three operations of

EDE are brieﬂy discussed below.

1) MUTATION

It expands the search space of the problem. For mutation three

vectors are selected randomly. One vector is taken as target

vector and the difference of other two vectors are multiplied

with mutation factor Fand the result is added to target vector

to get the mutant vector,

Vj=xr1+F×(xr2−xr3),(16)

where, Vjis a mutant vector, Xr1,Xr2and Xr3are the target

and other two randomly selected vectors to produce mutant

vector.

2) CROSSOVER

It incorporates successful solution from the previous gener-

ation. Once the mutation step is completed, EDE algorithm

performs crossover operation to produce trial vectors. The

trial vector is generated from the variables of mutant vectors

and variables of target vectors,

Uj1=(Vj,if rand(b)≤CR1,

xr1,if rand(b)>CR1,(17)

where, Uj1is the trial vector, CR represents crossover rate.

The variable to be selected for trial vector will be chosen from

target and trial vector while keeping (15) in consideration.

The difference between DE and enhanced DE (EDE) lies here

that in DE only one trial vector is generated to replace the

target vector selected from the population while in EDE ﬁve

trial vectors are generated. Three of which are produced at

different crossover rates as shown in the following equations,

Uj2=(Vj,if rand(b)≤CR2,

xr1,otherwise,(18)

Uj3=(Vj,if rand(b)≤CR3,

xr1,otherwise.(19)

The two trial vectors are generated below,

Uj4=rand(b)∗xr1,(20)

Uj5=rand(b)∗Vj+(1 −rand (b)) ∗xr1.(21)

3) SELECTION

The trial vectors and target vector are test by ﬁtness function.

Among six vectors; ﬁve trial vectors and one target vector,

the one which is ﬁttest will replace the target vector in the

population,

xr1=(Uji,if F(Uji )>F(xr1),

xr1,otherwise.(22)

Once the mutation, crossover and selection is completed the

algorithm will continue search for next ﬁttest value until

maximum iteration deﬁned reaches.

B. HSA

HSA is introduced by Geem et al. [16]in 2001. It is formed

from the concept of the improvisation process of a musician

in which the musician always search for perfect state of

harmony. The musician tries to ﬁnd pleasing harmony just as

optimization techniques search for global best solution. The

musician makes various combination of pitches stored in the

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TABLE 3. Parameters of HSA.

harmony memory. If all the pitches make a good harmony

one by one, those pitches are stored in the library/memory

and the chance of producing better harmony in the next try

increases. Same process happens in the engineering optimiza-

tion where each decision variable chooses initial values from

the search space and makes single solution vector. If this

solution vector is ﬁtter than the previous one, then search

for more better solution (global best) continues until stopping

criteria reaches. The parameters used in HSA are deﬁned in

Table 3.

The HS algorithm requires less mathematics and no initial

value setting of the decision variables. The main steps of

HS algorithm are

1) Harmony memory generation

2) Improvisation of new harmony from HM

a) Harmony memory consideration rate (HMCR)

adjustment

b) PAdR adjustment

c) Random selection

3) Update HM

4) Repeat step 2 and 3 until stopping criteria reaches.

1) HARMONY MEMORY GENERATION

Initial HM is generated randomly,

Hij =Hl+rand(b)∗(Hu−Hl),(23)

where, Hij represents the set of random values in the HM,

Hland Huare the lower and upper limit of values in the HM.

2) IMPROVISATION OF NEW HM

A new harmony vector x0is produced from HM consid-

ering HMCR, PAR and random selection. Each value in a

new harmony vector is taken by comparing with HMCR

and PAdR values. The value of x0can be chosen from

x0

1,x0

2,x0

3, ...., x0

nwith probability of HMCR and 1-HMCR,

these values can be taken from the entire feasible region xij.

x0=(x0

i∈[x0

1,x0

2,x0

3· ··,x0

n],with prob HMCR,

x∈Hij with prob 1 −HMCR.

(24)

Each value in the new harmony vector x0is checked whether

it should be pitch adjusted or not.

pitch adj for x 0

i=(yes,with prob PAdR,

no,with prob 1 −PAdR.(25)

The value of probability (1-PAdR) shows that no adjustment

is required for the variable but if the pitch adjustment check

is satisﬁed, then the variable x0

iis adjusted below,

x0

i=x0

i+rand(b)∗bw,(26)

where, bw is the arbitrary distance bandwidth.

3) UPDATE THE HM

The new generated harmony vector x0=[x0

1,x0

2,· · ··,x0

n]

is compared with the worst harmony vector in HM using

objective function, in our case minimum cost is ﬁttest one.

If the new vector is better than worst one then new one will

take place of the worst one in the memory.

4) STOPPING CRITERIA

Once the maximum number of iterations are completed, the

algorithm stops its execution.

C. HYBRID HSDE ALGORITHM

The hybrid HSDE has the common features of both EDE

and HSA in it. The random selection step of HS algorithm

is replaced by the ﬁtness check steps of EDE which provides

a good result in terms of user comfort and PAR reduction.

The steps of HSDE are same as HS algorithm except the

improvisation of harmony the random selection is replaced

by ﬁtness evaluation of the new harmony vector and ran-

domly selected target vector. The ﬁttest vector is stored in the

harmony memory and then search for the next ﬁttest vector

starts until some stopping criterion is reached. The working

principle of HSDE algorithm is summarized in Algorithm 1.

The parameters used in HSDE are deﬁned in Table 4.

The target vector for evaluation of ﬁtness is selected in the

following equation,

Hr1=Vr1+F×(Vr2−Vr3),(27)

where, Hr1is mutant vector, Vr1is target vector, Fis mutant

factor, Vr2and Vr3are other two randomly selected vectors

from HM. After mutation, crossover step is followed same

as in EDE. Here only three trial vectors are generated for

evaluation so the computational time is reduced,

Hj1=(Hr1,if rand(b)≤CR1,

Vr1,otherwise,(28)

Hj2=(Hr1,if rand(b)≤CR2,

Vr1,otherwise,(29)

Hj3=(Hr1,if rand(b)≤CR3,

Vr1,otherwise.(30)

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TABLE 4. Parameters of HSDE.

The selection of trail vector in HSDE will be same as in EDE

that the ﬁttest value among the trial vectors, target vector and

new generated harmony vector will be saved in the memory.

Hij =

x0,if F(Hj)<F(x0)>F(Vr1),

Hj,if F(Vr1)<F(Hj)>F(Vr1),

Vr1,if F(Hj)<F(Vr1)>F(Vx0).

(31)

VI. FEASIBLE REGIONS

The feasible region contains the set of possible solutions

which satisfy all the constraints of a system. In this work

the feasible regions for cost vs waiting time termed as user

comfort and cost vs load are deﬁned. The scheduler ensures

that the power consumption should be done in such a way that

the cost does not exceed the range deﬁned in feasible region.

The feasible region for waiting time and cost have trade-off

behavior as shown in Fig. 2. When the delay is maximum,

the electricity bill lies in the range between 28.05 −57.54

cents for minimum and maximum loads as shown by P2and

P3respectively. While the electricity cost is maximum when

the waiting time is zero with maximum and minimum power

consumption as shown by points P5and P1respectively.

Fig. 3 shows feasible region for electricity cost and elec-

tricity consumption with the help of P1,P2,P3,P4and

P5forming a trapezoidal shape. Point P1represents the

electricity bill when minimum possible energy consumption

(minimum load) is scheduled with minimum price value in

the RTP signal. Point P2shows the electricity bill when

minimum load is scheduled with maximum price value in the

RTP signal. Similarly points P4and P5represent the elec-

tricity bills when maximum load (all operating appliances)

are scheduled with minimum and maximum price values

in the RTP signal. As the pricing signal is always set by

the utility company and the consumer can never change or

modify it, the consumer can only schedule their appliances

accordingly so that maximum saving could be achieved.

We have put a constraint that the scheduler must always

schedule the appliances of the smart home in such manner

that the cost at any time-slot must not exceed 350.6 cents.

The point P3is the point of constraint given to the scheduler.

VII. SIMULATION RESULTS

In this section, we discuss the simulation results and ana-

lyze the performance of the scheduling algorithms in term

of electricity cost savings, user comfort and PAR. We take

eleven different appliances with different energy demands

Algorithm 1 HSDE Algorithm

1Initialize algorithm parameters (HMCR, HMS, CR,

PAdR, bw, F, I)

2Generate the harmony memory

3for {p=1:NI} do

4Find f(xworst )

5Generate a new harmony vector (x0

i)

6for x0

i=1:HMS do

7if rand (1)< HMCR then

8x0=HM[i][j] where i ∈(1, 2, . . . , HMS) if

rand(1) < PAdR then

9x0

i=x0

i* rand(1) * bw

10 end if

11 end if

12 else

13 Select three harmony vectors to generate

mutant and trial vectors

14 Mutation

15 Vj=xr1+F×(xr2−xr3)

16 Crossover

17 Uj1(Vjif rand(b)≤CR1

xr1Otherwise

18 Check for the ﬁttset one among trial vector

if f(Vj)<f(x0)then

19 Replace xworst with trial vector Else

Replace xworst with x0

20 end if

21 end if

22 end for

23 Update the harmony for next iteration

24 end for

25 Continue until termination criteria reaches

FIGURE 2. Feasible region cost vs waiting time.

and different operation times normally used in the homes.

RTP pricing signal has been used for billing purpose. The

simulation is performed for the time period of 24 hours.

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FIGURE 3. Feasible region cost vs load.

FIGURE 4. RTP.

FIGURE 5. Forecasted daily temperature.

Fig. 4 and Fig. 5 are forecasted input pricing and temper-

ature signals given to the HEM system from utility company

and METEONORM 6.1 for Islamabad region of Pakistan.

The forecasted outdoor temperature is formulated to inform

FIGURE 6. Solar irradiance.

FIGURE 7. PV system generation.

the scheduler how much generation is possible for that day.

The RTP signal is made by utility on the average consumption

behavior of the consumers for the last three months. The price

in each hour is changing and from 7 :00 a.m to 3 :00 p.m it

is comparatively high and expensive. Similarly temperature

forecasted for 24 hours is high at noon.

The conversion efﬁciency of generator, area of genera-

tor, solar irradiance and outdoor temperature are the factors

effecting the generation of PV system as modeled in (6).

The 90% of estimated power generated by PV system is

utilized by the consumers at day time to reduce their elec-

tricity bill and the remaining 10% is utilized by system to

facilitate integration complexities. Fig. 6 and Fig. 7 present

the solar irradiance and the estimated electricity generation

from PV system.

Fig. 8a shows the hourly electricity consumption without

RES and ESS integration. Peaks are formed in unscheduled

scenario and the three algorithms have optimized the con-

sumption by uniformly distributing the load over the schedul-

ing horizon. Though EDE and HSA has shifted maximum

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FIGURE 8. Energy consumption per hour. (a) Energy consumption per

hour without RES. (b) Energy consumption per hour with RES.

load to the ending time-slots, however, their peaks are still

lower than peaks of unscheduled pattern. Peaks are formed

in unscheduled scenario in the time-slots 8 and 15 touching

17.8, and 18.9 kWh, respectively. The hybrid technique, i.e.,

HSDE shows moderate behavior throughout the scheduling

horizon. HSA shows some peaks in the ending time-slots 15,

16 and 18, it has maximum electricity consumption

of 16.2 kWh. It has shown minimum and negligible consump-

tion in the starting hours, i.e., 1 −6. EDE based scheduling

technique reduces the peaks and provides moderate consump-

tion pattern. It has minimum electricity consumption in time-

slot 1 and 3, i.e., 4.6 and 5.1 kWh, respectively, and maximum

value of consumption in any time-slot is 15.6 kWh in the

time-slots 19 −24.

The hourly energy consumption patterns of unscheduled

and scheduled load along with RES and ESS integration are

presented in Fig. 8b. The unscheduled load pattern has a

peaks of 12.9, 11.95 and 14 kWh in time-slots 3, 6 and 22,

respectively. It has minimum consumption in time-slot 9 and

zero consumption from utility in time-slots 1, 11 and 24.

Rest of the time-slots are showing the moderate consump-

tion. Using HSA technique with RES and ESS, the peaks

in unscheduled pattern are reduced up to 7.5 kWh in time-

slot 16. As compared to unscheduled energy consumption

pattern, the per hour energy consumption in HSA is optimum.

The consumption is maximum, i.e., 7.5 kWh in time-slot 16

and minimum in time-slots 2 −6, and 8, i.e., 1.7 and

2.7 kWh respectively. In time-slots 1 and 24 there is zero

energy consumption. Rest of the time-slots have average

consumption pattern. EDE based scheduling shows some

peaks in the starting and ending hours but the overall elec-

tricity consumption pattern is moderate. The consumption

pattern of EDE shows peaks in time-slots 8 and 17 −24.

The minimum consumptions is in time-slots 9, 13 and 14

are 1.5, 2.4 and 2.4 kWh, respectively, and in time-slots

8 and 11 negligible amount of electricity is consumed from

the utility. Our proposed hybrid technique, i.e., HSDE also

has peaks at the starting and ending time-slots but when

RES and ESS are integrated the consumption pattern shows

minimum and negligible behavior in time-slots 7 −17. The

maximum electricity consumption of 11.5 kWh is shown in

time-slot 23 which are followed by other peaks in time-slots

3, 6, 18 and 24. The energy consumption in these time-slots

is 9, 9, 9.8 and 10.9 kWh, respectively.

The hourly electricity bill of unscheduled and scheduled

load without RES and ESS is shown in Fig. 9a. Results show

that the bill of heuristic algorithms (HSA, EDE and HSDE)

based scheduling remains within the feasible region. From

the ﬁgure it can be seen that unscheduled scenario results in

maximum bill. In time-slot 8, the electricity cost is 475 cents

which is maximum among all. The three proposed techniques

(HSA, EDE and HSDE) have reduced this electricity cost

considerably. In the same time-slot the maximum cost of each

algorithm falls which is 28.84%, 38.72% and 49.89% less

than unscheduled cost. In the starting time-slots 1 −7, the

electricity cost is comparatively lower than last time-slots.

The electricity bill of scheduled load is less than unscheduled

load.

The hourly electricity bill of unscheduled and scheduled

loads with RES and ESS is shown in Fig. 9b. This shows

that the electricity bills of scheduling algorithms (HSA, EDE

and HSDE) is less as compared to unscheduled cost. The

overall cost in all the time-slots is also less than that in

Fig. 9a. The energy consumption pattern of unscheduled

scenario is most expensive in the graph e.g. in time-slot 9

the cost of unscheduled pattern is 195 cents. This expensive

peak is reduced by all the three algorithms up to 45, 40

and 60%. HSDE has maximum cost in 6th hour of a day, i.e.,

116.3 cents that is 43% less than the peak of unscheduled

cost. HSA has maximum cost in 7th hour of a day, i.e.,

90 cents that is 55% less than unscheduled maximum cost

and EDE has maximum cost of 100 cents in 4th hour of a day

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FIGURE 9. Electricity cost per hour. (a) Bill per hour without RES. (b) Bill

per hour with RES.

that is 49.5% reduced than unscheduled cost. HSDE has zero

cost in 8th and 9th time-slots which means in these hours the

total energy is consumed from RES and ESS. Similarly, EDE

has zero consumption in time-slots 8 and 11.

Peak formation is a major drawback in traditional electric

power system as it causes customer to pay high electricity

bill as well as challenges the stability of grid. Performance

of all the designed models (HSA-EMC, EDE-EMC and

HSDE-EMC) with respect to PAR reduction is shown in

Fig. 10a. It shows that PAR is signiﬁcantly reduced

by HSA-EMC, EDE-EMC and HSDE-EMC than the

unscheduled case because these are designed to avoid peak

formation.

When RES and ESS are integrated the load pattern

becomes smooth and the peaks are reduced. The smart user

does not fully rely on the utility grid, instead prefer the use

of RES and ESS when available. By doing this both cost

and PAR are reduced resulting in grid stability. In Fig. 10b

the PAR values after integration of RES and ESS are shown

which are lower than Fig. 10a.

FIGURE 10. PAR. (a) PAR without RES. (b) PAR after RES.

TABLE 5. Performance trade-off comparison.

The comparison of overall daily electricity bill of the

unscheduled and scheduled load without RES and ESS is

shown in Fig. 11a. The daily electricity cost in unscheduled,

and in scheduled load scenarios using HSDE, HSA, and

EDE algorithms are 3652.63, 3163.39, 3000.02, and 3227.61

cents respectively. The comparison of total electricity cost

shows that HSDE, HSA, and EDE algorithms based HEM

system reduces the electricity bill by 13.2%, 17.86%, and

11.5% respectively. In Fig. 11b, daily electricity costs after

integration of RES and ESS are shown. Compare to Fig. 11a

the electricity bill bars of Fig. 11b are shorter. HSA along

with RES and ESS gives minimum cost than others.

A. TRADE-OFF BETWEEN PARAMETERS

The trade-off behavior explained in Section VI is summarized

in Table 5 as shown in Figs. 11 and 12. HSA has maximum

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TABLE 6. The overall comparison of the algorithms.

FIGURE 11. Daily cost. (a) Daily cost without RES. (b) Daily cost with RES.

waiting time (less user comfort) which has minimum cost

among the three algorithms, i.e., 3000.02 cents in Fig. 11

while EDE entertains the user with maximum comfort giving

less saving in electricity bill, i.e., 425 cents. Our proposed

HSDE algorithm presents less waiting time than HSA.

The electricity bill offered by this algorithm is also expensive

then HSA. Results reveal that integration of RES and ESS

minimizes the electricity bill up to 52% daily. Peaks in the

electricity consumption pattern are smoothen and the per

hour consumption of all three algorithms is invulnerable to

utility and consumer. HSDE shows balanced load pattern than

HSA and EDE which increases the power system stability.

FIGURE 12. Waiting time.

Reduction in PAR by HSDE, HSA and EDE is 17.247%,

32.871% and 16.586% respectively. Furthermore our simu-

lation verify the feasible regions which is provided before

the simulation for optimal solution. On the other hand, there

exists a trade-off between electricity cost and user waiting

time which is depicted in our work.

The overall performances of the algorithms before and

after RES integration is shown in Table 6.

VIII. CONCLUSION

Due to lack of energy sources and aging of existing power

systems, demand for smarter and efﬁcient power system has

increased. The concept of SG has been introduced for this

reason in which the appliances are made smart in such a way

that they can coordinate through EMC and even control the

power consumption of smart home. The integration of RES

and ESS into SG maximizes the user comfort by economi-

cally consuming electricity in the peak hours. In this paper a

new hybrid algorithm HSDE is proposed by hybridizing two

existing heuristic algorithms (HSA and EDE). Their perfor-

mance on the bases of PAR, electricity cost, user comfort and

energy consumption is evaluated. To tackle the intermittent

behavior of RES and implementing RES with future work is

another direction of our work.

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SAQIB KAZMI received the M.S. degree in electrical engineering from

the Department of Electrical Engineering, COMSATS Institute of Electrical

Engineering, Islamabad, Pakistan, under the supervision of Prof. J. Mughal

and Dr. N. Javaid.

NADEEM JAVAID (S’08–M’11–SM’16) received

the bachelor’s degree in computer science from

Gomal University, Dera Ismail Khan, in 1995,

the master’s degree in electronics from Quid-i-

Azam University, Islamabad, Pakistan, in 1999,

and the Ph.D. degree from the University of Paris-

Est, France, in 2010. He is currently an Associate

Professor and the Founding Director of Communi-

cations over Sensors Research Lab, Department of

Computer Science, COMSATS University Islam-

abad, Islamabad. He has supervised 15 Ph.D. dissertations and 100 master’s

theses. He has authored over 700 articles in technical journals and inter-

national conferences. He is also an Associate Editor of the IEEE ACCESS

Journal and an Editor of the International Journal of Space-Based and

Situated Computing. His research interests include energy optimization in

block chain based smart grids and IoT enabled wireless sensor networks, and

data analytics in smart grids/wireless sensor networks. He was a recipient of

the Best University Teacher Award from the Higher Education Commission

of Pakistan, in 2016, and the Research Productivity Award from the Pakistan

Council for Science and Technology, in 2017.

MUHAMMAD JUNAID MUGHAL is currently

the Chairman of the Department of Electrical

Engineering, COMSATS Institute of Information

Technology, Islamabad, Pakistan.

MARIAM AKBAR (S’13–M’16) received the

M.Sc. and M.Phil. degrees from Quid-I-Azam

University, Islamabad, and the Ph.D. degree in

electrical engineering from the COMSATS Insti-

tute of Information Technology, Islamabad, under

the supervision of N. Javaid. She is currently an

Assistant Professor with the Department of Com-

puter Science, COMSATS Institute of Information

Technology. Her research interests include wire-

less networks and smart grids.

24280 VOLUME 7, 2019

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SYED HASSAN AHMED (S’13–M’17) received

the B.S degree in computer science from the

Kohat University of Science and Technology,

Pakistan, and the master’s and Ph.D. degrees

from the School of Computer Science and Engi-

neering (SCSE), Kyungpook National Univer-

sity (KNU), South Korea. In 2015, he was also

a Visiting Researcher with the Georgia Institute

of Technology, Atlanta, USA. He is currently a

Post-Doctoral Research Fellow with SCSE, KNU,

where he also teaches ’’Design and Analysis of Computer Networks’’ course

at the Graduate School. He authored or co-authored over 90 International

journal articles, conference proceedings, book chapters, and two Springer

brief books. His research interests include sensor and ad hoc networks, cyber

physical systems, vehicular communications and future Internet. He received

the Research Contribution awards by SCSE at KNU, from 2014 to 2016, and

the Qualcomm Innovation Award at KNU, in 2016.

Dr. Hassan is a member of ACM. He is serving as a TPC Member

or Reviewer in more than 50 International Conferences and Workshops,

including IEEE Globecom, IEEE ICC, IEEE CCNC, IEEE ICNC, IEEE

VTC, IEEE INFOCOM, ACM CoNEXT, ACM SAC, and much more. He

has been reviewing papers for more than 30 International Journals, including

the IEEE WIRELESS COMMUNICATIONS MAGAZINE, the IEEE NETWORKS MAGAZINE, the

IEEE COMMUNICATIONS MAGAZINE, the IEEE COMMUNICATIONS LETTERS, the IEEE

SENSORS LETTERS, the IEEE TRANSACTIONSON INDUSTRIAL INFORMATICS,Vehicular

Technologies,Intelligent Transportation Systems Big Data,Mobile Comput-

ing, Elsevier Computer Communications, and Computer Networks.

NABIL ALRAJEH received the Ph.D. degree

in biomedical informatics engineering from

Vanderbilt University, USA. He was a Senior

Advisor for the Ministry of Higher Education, and

his role was in implementing development pro-

grams, including educational affairs, health infor-

mation systems, strategic planning, and research

and innovation. He is currently a Professor with

the Health Informatics, Biomedical Technology

Department, King Saud University. His research

interests include E-health applications, hospital information systems,

telemedicine, intelligent tutoring systems, energy management in smart

grids, and wireless sensor networks.

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