journal paper presentation › sahar_paper.pdf · comsats institute of information technology,...

Exploiting heuristic algorithms to efficiently utilize

energy management controllers with renewable energy

sources

COMSATS Institute of Information Technology, Islamabad, Pakistan

Journal Paper Presentation

Contents Introduction

Related work

Motivation

Proposed system model

1) Load categorization

2) Energy consumption model

3) Energy price model

4) Local energy generation

5) Energy storage system

6) Residential users classification

Problem formulation

1) MKP

2) PAR

3) Waiting time

4) Objective function/Heuristic algorithms

Simulations and results

Conclusion and future work2/9/20172


2/9/20173

Drawbacks in Traditional Power Grid Unintelligent electricity system

Conventional power grid suffers lots of economical losses

Contributing factors and its consequences include*:

1) Aging equipment:

Unreliable--higher failure rates

Customer interruption rates--maintenance costs, repair and restoration costs

2) Obsolete system layout:

Require serious additional and smart substation

Lack of computational abilities

Lack of communication abilities

3) Outdated engineering:

Traditional tools for power delivery

Lack of smart electronic control and sensors

Lack of storage system

Lack of energy management systems

Introduction (1/3)

COMSATS Institute of Information Technology, Islamabad, Pakistan*www.sciencedirect.com/science/article/pii/S0378778816306867

2/9/20174

Smart grid (Evolutionary power grid) Infrastructure that supports*

1) Advanced electricity generation, delivery, and consumption

2) Advanced information metering, monitoring, and management

3) Advanced communication technologies

Steps for conceptual design of a

smart grid (SG) as in fig. 1**

1) Power system in real time

2) Increasing system capacity

3) Eliminating bottlenecks

4) Enabling a self healing system

5) Enabling connectivity to consumers

Fig. 1: SG architecture

Introduction (2/3)


*V. Gungor, D. Sahin, T. Kocak, S. Ergut, C. buccella, C. Cecati, G. Hancke, Smart grid technologies: communication technologies and standards, IEEE Trans Ind. Inform. 7

(November (4)) (2011) 529–539

**S. Rahim, N. Javaid, A. Ahmad, S. A. Khan, Z. A. Khan, N. Alrajeh,U. Qasim, Exploiting heuristic algorithms to efficiently utilize energy management controllers with

renewable energy sources, Energy and Buildings 129 (2016) 452–470

2/9/20175

Brief comparison between traditional grid and SG*Introduction (3/3)


*S. Rahim, N. Javaid, A. Ahmad, S. A. Khan, Z. A. Khan, N. Alrajeh,U. Qasim, Exploiting heuristic algorithms to efficiently utilize energy

management controllers with renewable energy sources, Energy and Buildings 129 (2016) 452–470

Infrastructures Traditional grid SG

Power system • Centralized generation.

• Uni-directional power transmission

(utility to consumer)

• Uni-directional information flow

(utility to consumer)

• Low storage capacity

• Distributed generation

• Bi-directional power transmission (utility to

(from) consumer)

• Bi-directional information flow (utility to (from)

consumer)

• Grid energy storage capacity

Information technology • Aged metering system

• No monitoring system

• Lack of management units

• Advanced metering system (advanced metering

infrastructure)

• Smart monitoring (phasor management unit)

• Information management unit

Communication system Wired technology Wired and wireless technologies

Energy sources system Non-renewable sources (mainly fossil fuel

and atomic energy)

Both non-renewable and renewable sources

(photovoltaic panels, wind turbine, plug-in electric

vehicles, etc.)

Power losses control

system

Wastage of electricity due to limited

power storage

Efficient use of electricity minimizes power losses

6

Authors in [1] purpose a model for home energy management controller for residential users: MKP

Objectives: Reduce electricity bill and peak formation

Contribution: set priorities to schedule appliance accordingly

Integration of RES

An efficient model for energy management system by using HAN is presented in [2]: GA

Objectives: PAR reduction and electricity bill minimization

Contribution: used RTP tariff combine with IBR

User comfort ignored

RES not considered

Literature Review (1/3)

1] O. A. Sianaki, O. Hussian and A.R. Tabesh, “A Knapsack Problem Approach for Achieving Efficient Energy Consumption in Smart Grid for

End-user Life Style”, IEEE conference, Waltham, MA, Sept. 2010.

[2] Z. Zhao, W. C. Lee, Y. Shin and K. Song, “An Optimal Power Scheduling Method for Demand Response in Home Energy Management

System”, IEEE Transaction Smart Grid, Vol. 4, No. 3, pp. 1390-1400, Sept 2013.

COMSATS Institute of Information Technology, Islamabad, Pakistan 2/9/2017

7

In [3], real time model for optimal power usage of household

appliances is proposed: BPSO

Objectives: Energy saving and electricity cost reduction

Contribution: appliance categorization

User comfort not considered

Integration of RES

Authors in [4] proposed an efficient scheme to manage congestion

problem in SG through DR: ACO

Objectives: minimize cost and maximize user comfort

Contribution: use fuzzy technique to choose most feasible solution

Integration of RES


2/9/2017COMSATS Institute of Information Technology, Islamabad, Pakistan

[3] M. A. A. Pedrasa, T. D. Spooner and I. F. MacGill, “Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization”, IEEE Transactions on Power Systems, Vol.

24, No. 3, pp. 1173 - 1181, Aug. 2009.

[4] J. Hazra, K. Das and D. P. Seetharam, “Smart Grid Congestion Management through Demand Response”, 2012 IEEE Third International Conference on Smart Grid

Communications, Tainan, pp. 109 - 114, 5 - 8 Nov. 2012.

Reference Objective (s) Techniques Results Deficiency (ies)

A Knapsack problem approach for

achieving efficient energy consumption

in smart grid for end users life style [1]

Reduction in

electricity bills

and peak

formation

MKP

+

Dynamic

Programming

Efficiently manage

peak hours with

considering user

comfort level

Integration of RES.

An Optimal Power Scheduling Method

for Demand Response in Home Energy

Management System [2]

Electricity bills

and PAR

reduction

GA

+

RTP+IBR

Effectively reduce

PAR and electricity

cost

Ignorance of user

comfort level and

integration of RES

Scheduling of Demand Side Resources

Using Binary Particle Swarm

Optimization [3]

Energy saving and

electricity cost

reduction

BPSO Reduce bills and

PAR minimization

User comfort

ignored

Integration of RES

Smart Grid Congestion Management

through Demand Response [4]

Congestion

problem with

electricity cost

minimization

ACO

+

Fuzzy

Techniques

Reduce PAR and

electricity cost

Integration of RES

2/9/20178

[1] O. A. Sianaki, O. Hussian and A.R. Tabesh, “A Knapsack Problem Approach for Achieving Efficient Energy Consumption in Smart Grid for End-user Life Style”, IEEE conference, Waltham, MA, Sept.

2010.

[2] Z. Zhao, W. C. Lee, Y. Shin and K. Song, “An Optimal Power Scheduling Method for Demand Response in Home Energy Management System”, IEEE Transaction Smart Grid, Vol. 4, No. 3, pp. 1390-

1400, Sept 2013.

[3] M. A. A. Pedrasa, T. D. Spooner and I. F. MacGill, “Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization”, IEEE Transactions on Power Systems, Vol. 24, No. 3, pp. 1173 -

1181, Aug. 2009.

[4] J. Hazra, K. Das and D. P. Seetharam, “Smart Grid Congestion Management through Demand Response”, 2012 IEEE Third International Conference on Smart Grid Communications, Tainan, pp. 109 -

114, 5 - 8 Nov. 2012.



2/9/20179

Existing Optimization

Problems*

Minimize the Electricity Bill.

Minimize both Electricity Bill and Aggregated

Power Consumption.

Minimize Peak to average ratio

(PAR).

Maximize User Comfort.

Efficient Integration of

Renewable Energy sources

(RESs).

Minimize the Aggregated

Power Consumption.

Motivation


Fig. 2*: Proposed SG Model



2/9/201710

Proposed System Model (1/6)




Fig. 3*: Proposed DSM functional diagram.

Pictorial representation of DSM model for our proposed scheme

2/9/201711

Fixed appliances: Regular appliances

Usage can not be modified

Total power consumed,

𝜈𝑇 = 𝑓𝑒𝑑∈𝐹𝑒𝑑

𝑡=1

24

𝜌𝑡𝑓𝑒𝑑

× 𝜒𝑓𝑒𝑑 𝑡 … (1)

Appliances 𝜌𝑎 (kWh)

Lighting 0.6

Fans 0.75

Microwave oven 1.18

Toaster 0.5

Coffee maker 0.8

Load classification*

Fixed appliances

(lights, fans, oven, toaster, tv, etc.)

Shiftable appliances

(washing machine, dish washer,

clothes dyer, etc.)

Elastic appliances

(air conditioner, water heater,

space heater, etc.)

Status of all appliances:

𝜒𝑓𝑒𝑑(𝑡), 𝜒𝑠𝑒𝑑(𝑡), 𝜒𝑒𝑒𝑑(𝑡)= 1 𝐼𝑓 𝑎𝑝𝑝𝑙𝑖𝑎𝑛𝑐𝑒 𝑖𝑠 𝑂𝑁0 𝐼𝑓 𝑎𝑝𝑝𝑙𝑖𝑎𝑛𝑐𝑒 𝑖𝑠 𝑂𝐹𝐹





2/9/201712

Shiftable appliances:

Burst load

Manageable

𝛼𝑠𝑒𝑑 ≤ 𝜏𝑠𝑒𝑑 ≤ 𝛽𝑠𝑒𝑑 The total power consumption,

𝜗𝑇 = 𝑠𝑒𝑑∈𝑆𝑒𝑑

𝑡=1

24

𝜌𝑡𝑠𝑒𝑑

× 𝜒𝑠𝑒𝑑 𝑡 … (2)

Appliances 𝜶𝒂

(hours)

𝜷𝒂

(hours)

𝝋𝒂

(hours)

𝝆𝒂

(kWh)

Washing

machine

8 16 5 0.78

Dish washer 7 12 5 3.60

Clothes dyer 6 18 5 4.40

Appliances 𝜶𝒂

(hours)

𝜷𝒂

(hours)

𝝆𝒂

(kWh)

Air conditioner 6 24 1.44

Water heater 6 24 4.45

Space heater 6 24 1.50

Elastic appliances:

Interruptible appliances

Fully controllable (usage time and power

consumption profile)

Power consumption of each elastic appliance;

𝜁𝑡𝑒𝑒𝑑=

𝜆𝑒𝑒𝑑 × 𝜌𝑡𝑒𝑒𝑑

… (3)

Total power calculated by:

𝜅𝑇= 𝑒𝑒𝑑∈𝐸𝑒𝑑

𝑡=1

24

𝜁𝑡𝑒𝑒𝑑

× 𝜒𝑒𝑒𝑑 𝑡 … (4)



2/9/201713

Energy consumption model*:

Let:

o A= [𝑎1 , 𝑎2, 𝑎3, … , 𝑎𝑀]

o t ∈ 𝑇 = [1h, 2h, 3h,… , 24h]

Hourly energy consumption of each

appliance is given as,

𝐸𝑎 𝑡

= 𝐸𝑎,𝑡1 + 𝐸𝑎,𝑡2 + 𝐸𝑎,𝑡3 +⋯+ 𝐸𝑎,𝑡24 …(5)

Then, per day energy consumption demand

is calculated by,

𝐸𝑇 =

𝑡=1

24

𝑖=1

𝑀

𝐸 𝑎𝑖,𝑡 …(6)

Energy price model*:

Time of use (TOU) with power dependent tariff

i.e., inclined blocked rate (IBR) model

Let, the total power consumption by single user is

Δ𝑇 = 𝜈𝑇 + 𝜗𝑇 + Δ𝑇 …(7)

To calculate electricity bills,

Υ =

Υ1 0 ≤ Δ𝑇 ≤ Δ𝑡ℎ1

Υ2 Δ𝑡ℎ1 ≤ Δ𝑇 ≤ Δ𝑡ℎ2

Υ3 Δ𝑡ℎ2 < Δ𝑇

...(8)

Where,

Υ1, Υ2, Υ3 are different cost rate

Δ𝑡ℎ2 , Δ𝑡ℎ2are energy thresholds





2/9/201714

Local energy generation*: Generated renewable energy source (RES)

energy is

𝜓𝑟 𝑡 =1

2𝜋𝜎𝑒𝑥𝑝 −

𝑡−𝜇 2

2𝜎2 … (9)

The daily energy supply from RES is denoted by Θ

Θ 𝑡 = 𝑡=124 𝜓𝑟 𝑡 … (10)

0 ≤ 𝜓𝑟 𝑡 ≤ Θ𝑚𝑎𝑥 𝑡 ∀𝑡 ∈ 𝑇

Eligible for participation in some agreement with grid to sell power back to grid

𝜓𝑟𝑚𝑖𝑛 𝑡 ≤ Θ𝑚𝑎𝑥 𝑡

Energy storage system*:

Let number of battery used to store

electrical power energy belong to the set Γsuch that b ∈ Γ,

χby(t)= 1 charging0 discharging

…(11)

Charging and discharging rates are as ,

𝑟𝑏𝑦𝑐 < 𝑟𝑏𝑦

𝑐,𝑚𝑎𝑥× 𝜒𝑏𝑦

𝑟𝑏𝑦𝑑 < 𝑟𝑏𝑦

𝑑,𝑚𝑎𝑥× 1 − 𝜒𝑏𝑦

Despite the benefits of ESSs, their cost may

limit their applicability in real scenarios





Passive user:

Only consume electrical energy of

the grid

The energy consumption profile for

each user:

𝐸𝑖∈𝑃(𝑡) = 𝑡=124 𝐸𝑖 𝑡 … (12)

2/9/201715

Active users:

Take energy from RES and store it in storage

devices (batteries)

The energy consumption profile for 𝑡 ∈ 𝑇 is

calculated as:

𝐸𝑢∈𝑈(𝑡) = 𝑡=124 𝐸𝑢 𝑡 − Θ𝑢 𝑡 ± Γ𝑢 𝑡 … (14)

Semi-active users:

They consume energy both from power

grid and RES

The energy consumption profile for 𝑡 ∈𝑇 is calculated as,

𝐸𝑠∈𝑆(𝑡) = 𝑡=124 𝐸𝑠 𝑡 − Θ𝑠 𝑡 … (13)





Fig. 4*: End User classification.

Residential Users Classification

Formulate optimization problem → MKP

MKP as resource allocation problem

Mapped as follow*:

1) “j” number of knapsacks as power capacities in each time slot

2) Number of appliances as number of objects

3) The weight of each object as the energy consumed by appliances in each time slot

4) The value of object in a specific time slot is the cost of power consumption of the appliance

in that time slot

5) Value of binary variable “𝜒” can be 0 or 1 depending on the state of electrical appliance

6) Power capacity of grid in each time slot as 𝛾(𝑡) and given as,

𝑡=1

24

(𝐸(𝑡) × 𝜒(𝑡) ≤ 𝛾(𝑡))

𝜒 𝑡 ∈ 0,1

2/9/201716

Problem Formulation (1/5)Multiple Knapsack Problems (MKP)




PAR for single user is defined as the ratio of peak load and average load in each time

slot.

It is represented as 𝜙.

Mathematical form is as follow*,

𝜙 =max(∆(𝑡))

1

𝑇 𝑡=124 ∆(𝑡)

… (15)

Now, for “n” numbers of users,

𝜙𝑁 =max(∆(𝑡,𝑛))

1

𝑇( 𝑛=1

𝑁 𝑡=1𝑇 ∆ 𝑡,𝑛

… (16)

2/9/201717

Problem Formulation (2/5)Peak to Average Ratio (PAR)




2/9/201718

Ignored fixed appliances

User sets some parameters for each shiftable

and elastic appliance via user interface

The parameters are,

𝛼𝑎: Start time.

𝛽𝑎: End time.

𝜏𝑎: Length of operation.

Assume that 𝛽𝑎 − 𝛼𝑎 must be greater than

or equal to 𝜏𝑎 whereas, operation start time

𝜂𝑎 is variable obtained from heuristic

techniques

The range of 𝜂𝑎 is as: 𝜂𝑎 ∈ 𝛼𝑎 , 𝛽𝑎 − 𝜏𝑎

Problem Formulation (3/5)




Waiting Time*

Fig. 5*: Range of operational time

• User comfort depends upon

Waiting time reduction

Cost minimization

• Trade off between cost and waiting

time

• Waiting time is represented as 𝜑𝑎 and

calculated as*,

𝜑𝑎 =𝜂𝑎−𝛼𝑎

𝛽𝑎−𝜏𝑎−𝛼𝑎… (17)

2/9/201719


Fig. 6*: Waiting time




2/9/201720

min 𝑡=124 𝑤1. 𝑎=1

𝐴 ∆𝑎,𝑡 × 𝜒𝑎,𝑡 + 𝑤2 𝜑𝑎,𝑡 … (18)

s.t:

𝛼𝑠𝑒𝑑 ≤ 𝜏𝑠𝑒𝑑 ≤ 𝛽𝑠𝑒𝑑 (18a)

𝛼𝑒𝑒𝑑 ≤ 𝜏𝑒𝑒𝑑 ≤ 𝛽𝑒𝑒𝑑 (18b)

𝜂𝑎 ∈ 𝛼𝑎, 𝛽𝑎 − 𝜏𝑎 (18c)

𝜑𝑎 ≤ 5 (18d)

0 ≤ 𝜓𝑡𝑟 ≤ Θ𝑚𝑎𝑥 ∀𝑡 ∈ 𝑇 (18e)

𝑟𝑏𝑦𝑐 < 𝑟𝑏

𝑐,𝑚𝑎𝑥× 𝜒𝑏𝑦 ∀𝑏 ∈ 𝐵 (18f)

𝑟𝑏𝑦𝑑 < 𝑟𝑏

𝑑,𝑚𝑎𝑥× 1 − 𝜒𝑏𝑦 ∀𝑏 ∈ 𝐵 (18g)

𝑡=124 ∆𝑎,𝑡 × 𝜒𝑎,𝑡 ≤ 𝛾 ∀a ∈ 𝐴 (18h)

𝜒 𝑡 ∈ 0,1 ∀a ∈ 𝐴 (18i)

Objective function (Optimization function)*





• Designed objective function

aims to minimize electricity

bills while keeping under

consideration user comfort

level

• “w1” and “w2” are weights of

two parts of objective function

and their values are w1, w2 ∈ [0, 1]

w1+ w2 = 1

• It shows that either w1or w2

would be 0 or 1

2/9/201721

Heuristic Algorithms*

Proposed Solution (1/4)




• Due to highly volatile load behavior of

residential users and intermittent nature of

RESs

• Our defined problem is consider as as non-

linear optimization function

• To handle the complexity of our proposed

model, we apply three heuristic algorithms

and evaluate their results

• These algorithms are similar due to

population based search methods

• They move from one population to another

population in number of iterations with

improvement using a combination of

deterministic and probabilistic rules

Heuristic Algorithms

Genetic Algorithm (GA)

Binary Particle Swarm Optimization (BPSO)

Ant Colony Optimization (ACO)

2/9/201722

GA*





• Most suitable for complex non-linear models

• Probabilistic nature

• GA is used with dynamic tariff model (combined TOU with IBR) to get satisfactory

results

• In our modified algorithm, GA creates a random population initially

• Consisted of number of chromosomes that represent ON/OFF status of each

appliance

2/9/201723





BPSO*

• Used to solve global optimization problems.

• Ability to handle: Non-differential

Non-linear multimodal function

Parallel behavior

Ease of implementation

Good convergence properties

2/9/201724





ACO*

• It is a meta-heuristic optimization approach

• Solve discrete combinatorial optimization problems

• It has unique properties:

• Self-healing

• Self-protection and

• Self-organization

To evaluate different performance metrics of three Proposed energy

management controller (EMC) schemes*, we conduct extensive simulations in

MATLAB

Subject to fair comparison, we used TOU tariff model of Jemena Electricity

Networks (VIC) Ltd**

2/9/201725

Price rate for the peak hours is

14.884 cent/kwh

Shoulder peak hours is 9.298

cent/kwh

Off peak hours 4.370 cent/kwh

Simulation and Results (1/12)

Fig. 7*: TOU tariff model



COMSATS Institute of Information Technology, Islamabad, Pakistan** “Jemena Electricity Networks (VIC) Ltd - Network Tariffs For The 2015 Calendar Year (Exclusive of GST)”

2/9/201726

• Set important parameters to evaluate the performance of three different

heuristic based energy management controller (EMC) (GA-EMC, BPSO-EMC

and ACO-EMC)

• Parameters of GA-EMC is shown in table below,

Parameters (GA-EMC) Values

Population size 200

selection Roulette wheel

Elite count 2

Crossover 0.8%

Mutation 0.2%

Stopping criteria Max. generation

Max. generation 800





Parameters

2/9/201727

Parameters (BPSO-

EMC)

Values

Swarm size 10

Max. velocity 4 m/s

Min. velocity -4 m/s

Local pull (𝑐1) 2 N

Global pull (𝑐2) 2 N

Initial momentum weight 1.0 Ns

Final momentum weight 0.4 Ns

Stopping criteria Max. iteration

Max. iteration 600

Parameters (ACO-

EMC)

Values

Ant quantity 10

Pheromone intensity

factor

2

Visibility intensity factor 6

Evaporation rate 5

Trail decay factor 0.5

Stopping criteria Max. iteration

Max. iteration 600





Parameters

Execution time: Required time in which an algorithm

completes its functionality

Results show that GA-EMC<BPSO-

EMC<ACO-EMC as in table shown below,

2/9/201728

Execution time Values (second)

Without EMC 0.0983

GA-EMC 1.0191

BPSO-EMC 24.1933

ACO-EMC 77.7434





2/9/2017Research Symposium COMSATS Institute of Information

Technology 29

Unscheduled 266.3492(cents)

GA-EMC(RES) 75.4787(cents)

BPSO-EMC(RES) 90.4918(cents)

ACO-EMC(RES) 98.0409(cents)

Unscheduled 266.3492(cents)

GA-EMC 81.6097(cents)

BPSO-EMC 98.7183(cents)

ACO-EMC 114.2536(cents)

Without RES With RESFig. 9*: Electricity bills (cents)


2/9/201730





Fig. 8*: Energy consumption (kWh)

Used solar

panel

50% of load

demand

Maximum

unscheduled

load is

19.4250 kwh

Unscheduled 19.4250 (kwh)

GA-EMC 18.6750 (kwh)

BPSO-EMC 19.4250 (kwh)

ACO-EMC 19.4250 (kwh)

Unscheduled 19.4250 (kwh)

GA-EMC(RES) 18.6450 (kwh)

BPSO-EMC(RES) 18.8250 (kwh)

ACO-EMC(RES) 18.2450 (kwh)

Without RES With RES

2/9/201731





Fig. 10*: PAR curve

Fig. 11*: Waiting time rate

• Performance of all the designed models (GA-

EMC, BPSO-EMC and ACO-EMC) with

respect to PAR reduction is shown in Fig. 10

• It shows that PAR is significantly reduced for

GA-EMC, BPSO-EMC and ACO-EMC as

compared to the unscheduled due to

respective modified algorithm and designed

Eqs. 15

• GA-EMC is more effective in PAR reduction

due to its ability to generate new population

of more feasible solution using crossover and

mutation

• During scheduling horizon of shiftable

appliances, operational time is not fixed due

to price variation in dynamic pricing models

• By applying waiting time constraints (refer

Eqs. (18c) and (18d)) on the objective

function(refer Eq. (18)), we have enhanced

the performance of EMC in terms of user

comfort and electricity bill reduction

2/9/201732

Cases Total load

(kWh/day)

Total cost

(cent/day)

PAR

reduction

Cost reduction (%)

without RESs

Cost reduction (%)

with RESs

Without EMC 258 2201 244.6747 - -

GA-EMC 258 1127 81.8808 48.79 65

BPSO-EMC 258 1311 95.2281 40.43 57

ACO-EMC 258 1579 127.5380 28.26 52





Fig. 12*: Electricity bill per day. Fig. 13*: Electricity bill per day.

Parametric tuning for all models:

2/9/201733

population size 200, 1000 & 2000

crossover 1, 0.8 & 0.6

mutation 0, 0.2 & 0.4

Max. generation 2000, 1500, 1000 & 600

swarm size 10, 20 & 40

local pull factor 0, 1, 2 & 3

Global pull factor 4, 3, 2 & 1

Max. iteration 1800, 1500 & 600

Ant population 10 & 20

visibility intensity factor 6, 10 and 15

trial decay factor 0.5 & 1

Max. iteration 2000, 1500 & 600

GA-EMC BPSO-EMC

ACO-EMC





Proposed DSM model is beneficial for both utility and consumers.

GA-EMC acts more efficiently than BPSO and ACO to avoid peak

formation.

Energy management is cost-effectively achieved by satisfying end

user.

In term of execution time, GA-EMC<BPSO-EMC<ACO-EMC.

In future, we will work on Human behavior to achieve comfort

level of consumer.

Work on different optimization methods so that more accurate

data transformation is achieved with in less computational

complexity and time.

2/9/201737

Conclusion/Future Work


2/9/201738COMSATS Institute of Information Technology, Islamabad, Pakistan

journal paper presentation › sahar_paper.pdf · comsats institute of information technology,...

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