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Towards Green World: Renewable Energy Source based Energy Management in Residential Sector making Appliances, Homes and Buildings Smart- Final Defense Presentation

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Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Sakeena Javaid
CIIT/SP16-PCS-007/ISB
Supervisor: Dr. Nadeem Javaid (Associate Professor)
Co-Supervisor: Dr. Mariam Akbar (Assistant Professor)
Department of Computer Science
COMSATS University Islamabad, Islamabad
Towards Green World: Renewable Energy Source
based Energy Management in Residential Sector
making Appliances, Homes and Buildings Smart
Final Defense Presentation
Outline 2
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 3: Power Scheduling in Smart Homes based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Outline 3
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 3: Power Scheduling in Smart Homes based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Introduction (1/2) 4
[1] Energy Information Administration https://www.eia.gov/todayinenergy /detail.cfm?id=12251 United States
Department of Energy, Washington (last visited 17th December 2015).
[2] International Energy Outlook 2017, https://www.eia.gov/outlooks/ieo/pdf/0484 (2017). (last visited 29th
March 2019).
[3] https://www.google.com/imgres?imgurl=http%3A%2F%2Farticle.sciencepublishinggroup.com. (last
visited 29th March 2019).
Fig. 2: Energy consumption in residential sector [3].
World energy demand is increasing upto 2.5%
annually
Energy consumers over the globe (Five sectors)
According to a survey, this energy demand will
be increased upto 40%at the end of 2050 [1]
Quadrillion BTU
Fig. 1: Energy consumption graph [2].
Agriculture
[4] What Is Green Power. https://www.epa.gov/greenpower/what-green-power. (last
visited 29th March 2019).
Introduction (2/2) 5
Energy Generation Sources
Conventional Energy Resources
Renewable Energy Resources
Challenges
Conventional resources are more costly
UC is compromised
Increase of carbon emissions (serious ecological
and environmental concerns)
On-demand consumers’ requests are not
entertained efficiently in case of blackouts
Consumers’ Requirements
Cost minimization without compromising their
comfort
Integration of RESs with the grid power
Consumers behave as the prosumers Fig. 3: Energy Generation Sources [4].
UC: User Comfort
RESs: Renewable Energy
Sources
Outline 6
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 3: Power Scheduling in Smart Homes based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
7
[5] Rasheed, M. B., Javaid, N., Ahmad, A., Khan, Z. A., Qasim, U., & Alrajeh, N., (2015). An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes. Applied Sciences, 5(4),
1134-1163.
[6] Lu, X., Zhou, K., Chan, F.T. and Yang, S., (2017). Optimal scheduling of household appliances for smart home energy management considering demand response. Natural Hazards, 88(3), pp.1639-1653.
[7] Mahmood, D., Javaid, N., Ahmed, S., Ahmed, I., Niaz, I. A., Abdul, W., & Ghouzali, S. (2017). Orchestrating an effective formulation to investigate the impact of EMSs (energy management systems) for
residential units prior to installation. Energies, 10(3), 335.
[8] Ogunjuyigbe, A. S. O., Ayodele, T. R., & Akinola, O. A. (2017). User satisfaction-induced demand side load management in residential buildings with user budget constraint. Applied Energy, 187, 352-
366.
Literature Review (1/3)
Techniques
Objective(s)
Achievements
Limitations
WDO, BPSO
for HEM [5]
Single objective
function to reduce
cost
Cost minimization and
energy optimization
Peaks are created during OPHs,
UC is not calculated, only single
homes are focused
Scheduling of
household
appliances
using
MINLP
[6]
Single objective
function to minimize
cost
Cost reduction, energy
optimization and
inconvenience level
evaluation
Fixed power rating appliances
are considered, no regulating
appliances are considered, RESs
are not integrated, only one
household is considered
UC level
framework for
HEMS [7]
Single objective
function to minimize
cost
UC is calculated using the
appliances utility, electricity
cost savings and ROI
Appliances’ priority that may
account to UC is not considered,
multi
-objective optimization
functions are not considered
Load
satisfaction
algorithm [8]
Single objective
function to maximize
absolute satisfaction
User satisfaction calculated
on three postulation, cost
reduction is achieved
Appliances’ priority is not
considered, multi
-objective
optimization needs to be
considered
Table 1: Deterministic and Non-Deterministic
Optimization Techniques
WDO: Wind Driven Optimization
BPSO: Binary Particle Swarm
Optimization
MINLP: Mixed Integer Non-
Linear Programming
HEMS: Home Energy
Management System
UC: User Comfort
OPHs: Off Peak Hours
ROI: Return of Investment
8
[9] Keshtkar, A., Arzanpour, S., Keshtkar, F., and Ahmadi, P. (2015). Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives. Energy and buildings, 104,
165-180.
[10] Keshtkar, A., and Arzanpour, S. (2017). An adaptive fuzzy logic system for residential energy management in smart grid environments. Applied Energy, 186, 68-81.
[11 Li, M., Li, G. Y., Chen, H. R., & Jiang, C. W. (2018). QoE-aware smart home energy management considering renewables and electric vehicles. Energies, 11(9), 1-16.
[12] Fayaz, M., & Kim, D. (2018). Energy consumption optimization and user comfort management in residential buildings using a bat algorithm and fuzzy logic. Energies, 11(1), 1-22.
Techniques
Targeted Areas
Achievements
Fuzzy logic for
automating the
thermostat [9]
Residential
Buildings of one
city
Cost and energy
consumption
optimization using the thermostat
setpoints
Energy
management
by
fuzzy logic [10]
Residential
HVAC systems
Cost and energy
consumption
reduction using the
adaptable
autonomous thermostat setpoints
optimization
QoE
-Aware
Smart HEM
scheme
[11]
Residential
buildings
Cost and peak load reduction
with the certain users’
QoEs
Fuzzy
logic and
BAT algorithm
[12]
Residential
buildings
Energy savings and
comfort
e
nhancements
Table 2: Fuzzy Logic based Optimization
Techniques
Literature Review (2/3)
QoE: Quality of Experience
RES: Renewable Energy
Sources
PHs: Peak Hours
HEM: Home Energy
Management
9
[13] Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., & Wang, X. (2017). Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Transactions on Smart Grid, 8(4),
1943-1955.
[14] Yaghmaee , M. H., Leon-Garcia , A., Moghaddassian, M. (2016). On the performance of distributed and cloud-based demand response in smart grid. IEEE Transactions on Smart Grid, doi:
10.1109/TSG.2017.2688486.
[15] Chekired, D. A., & Khoukhi, L. (2017). Smart grid solution for charging and discharging services based on cloud computing scheduling. IEEE Transactions on Industrial Informatics, 13(6), 3312-3321.
[16] Al Faruque, M. A., & Vatanparvar, K. (2016). Energy management-as-a-service over fog computing platform. IEEE internet of things journal, 3(2), 161-169.
Techniques/Prototype
Targeted Areas
Achievements
Limitations
SA,
CoM, and MPL
[13]
Cloud computing,
energy
management
Cost and energy consumption
optimization
, optimized
resource allocation
Communication distance,
Consumers’ requests are delayed,
RT and PT are compromised
Cloud based DR and
distributed DR [14]
Cloud computing,
HEMSs
Cost and peak load reduction
through
optimized resource
allocation
Consumers requests are delayed,
RT and PT are required to be
optimized for each request
CPA, RPA [15]
Cloud computing,
EVs energy
management
Efficient charging and
discharging of EVs, Peak
load is reduced and grid
stability is maintained
RT and PT are compromised,
Effective RT and PT are
required, only EVs are
considered
Prototypes of HEM
[16]
Fog computing,
HEM
Efficient scheduling, cost
minimization, and intelligent
control and processing
Resources are limited, and more
storage capacity is required
Table 3: Cloud and Fog based Techniques
EVs: Electric Vehicles
SA: Simulated
Annealing
MPL: Modified Priority
List
CPA: Calendar Priority
Optimization
RPA: Random Priority
Optimization
RT: Response Time
PT: Processing Time
CoM: Cost-oriented
Model
Literature Review (3/3)
Outline 10
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 3: Power Scheduling in Smart Homes based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
11
Problem Statement
[5] Rasheed, M. B., Javaid, N., Ahmad, A., Khan, Z. A., Qasim, U., and Alrajeh, N. (2015). An efficient power scheduling scheme for residential
load management in smart homes. Applied Sciences, 5(4), 1134-1163.
[9] Keshtkar, A., Arzanpour, S., Keshtkar, F., and Ahmadi, P. (2015). Smart residential load reduction via fuzzy logic, wireless sensors, and smart
grid incentives. Energy and buildings, 104, 165-180.
[10] Keshtkar, A., and Arzanpour, S. (2017). An adaptive fuzzy logic system for residential energy management in smart grid environments.
Applied Energy, 186, 68-81.
[13] Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., & Wang, X. (2017). Optimal cloud computing resource allocation for demand side
management in smart grid. IEEE Transactions on Smart Grid, 8(4), 1943-1955.
[14] Gan, L., Topcu, U., & Low, S. H. (2013). Optimaldecentralized protocol for electric vehicle charging. IEEE Transactions on Power Systems,
28(2), 940-951.
[15] Chekired, D. A., & Khoukhi, L. (2017). Smart grid solution for charging and discharging services based on cloud computing scheduling.
IEEE Transactions on Industrial Informatics, 13(6), 3312-3321.
[16] Al Faruque, M. A., & Vatanparvar, K. (2016). Energy management-as-a-service over fog computing platform. IEEE internet of things
journal, 3(2), 161-169.
In the existing literature [5], [9], [10], to shift
appliances at an optimal operational time slot, static
priorities are assigned because of which the dynamic
adjustment of appliances with respect to change in
electricity prices and increase in load, is difficult.
Moreover, the load varies from situation to situation
and consumer to consumer [5],it is very hard to cope
with sudden changes in the load resulting in high user
discomfort and more electricity bills. Load
curtailment [9], is applied as a solution which also
results in the user discomfort during the high demand
intervals. Integration of RESs could be one of the
solutions
In addition, the work in [9], [10], authors consider
the energy management strategies for one country.
They do not focus on the energy optimization in
different meteorological conditions. This study is
Canada-oriented and authors have only optimized
the heat setpoints. However, they have neglected
the air conditioning system energy consumption
optimization in the same country and also for the
other countries.
For fulfilling the on-demand requests of
the consumers, instead of using their
EMCs, cloud and fog systems are
considered. Installation of EMCs on
individual bases becomes problematic due
to the limited number of appliances and
also a user is bound to buy the full
package. In this case, consumers’
electricity requests are delayed due to the
increasing size of requests (i.e., energy
demand) and high distance from the cloud
[13-16]. For enhancing the consumers’
RT, fog is introduced near the consumers’
locations, so that they can get efficient
services. EMC: Energy Management Controller
MGs: Micro Grids
Outline 12
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 2: Appliance Scheduling based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Proposed Solution 1
A New GWD Optimization Algorithm for DSM
in SG
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Javaid, S., Javaid, N., Javaid, Javaid, M. S., Javaid, S., Qasim, U., Khan, Z. A. (2016). Optimal scheduling in smart
homes with energy storage using appliances’ super-clustering, 10th IEEE International Conference on Innovative
Mobile and Internet Services in Ubiquitous Computing (IMIS-2016), Japan, pp: 342-348.
Javaid, N., Javaid, S., S., Abdul, W., Ahmed, I., A., Almari, A. and Niaz, I., A. (2017). A hyrid genetic wind driven
heuristic optimization algorithm for demand side management in smart grid, Energies, 10(3), pp: 1-27. [IF=2.676]
Challenges Addressed:
Energy optimization using appliances’ frame format
Comfort evaluation using multi-objective optimization
14
A New GWD Optimization Algorithm for DSM (1/5)
GWD: Genetic Wind Driven
SM: Smart Meters
AMI: Advance Metering
Infrastructure
HG: Home Gateway
SG: Smart Grid
PB: Priority Bit
System Model
Fig. 4: Work Flow of Proposed HEMC. Fig. 5: Frame Format for Appliance Scheduling.
Appliances are scheduled by
the frame format
15
Objective 1
O1 = Minimize(cost) 
Cost = Minimize

  
Constraints
 if t
if t 
1  
1  
Xi,t:Status of the appliance (ON, OFF)
PRi,t: Price of the energy consumed at time interval ‘t’
H1: PHs {7,8,9,10}
H2: OPHs (all others except H1)
: Energy consumed/power rating of the appliances at
any interval
Problem Formulation (1/3)
A New GWD Optimization Algorithm for DSM (2/5)
Objective 2
O2 = Maximize(UC)
UC = EappUtil + (EcostSavings/TotalCost) ... (2.1)
Constraints:
EappUtil = 
 (2.1.1)
EcostSavings =  
 
  
0.3  )
0.3 
 
 
16
Problem Formulation (2/3)
A New GWD Optimization Algorithm for DSM (3/5)
EappUtil: Minimum appliance delay
EcostSavings: Total cost savings

Schcost : Scheduled cost
Maxcost: Maximum cost
total_time=24 sub-timeslots
[8] Mahmood, D., Javaid, N., Ahmed, S., Ahmed, I., Niaz, I. A., Abdul, W., & Ghouzali, S. (2017). Orchestrating an effective formulation to investigate the impact of EMSs (energy
management systems) for residential units prior to installation. Energies, 10(3), 335.
17
Objective 3 [8]
O3 = Minimize(PAR)  
 
Objective Function
ObjFun = Minimize 


+
(O2) (4)
A New GWD Optimization Algorithm for DSM (1/5)
c1, c2: weights

Problem Formulation (3/3)
For the optimization of
cost and comfort by
assigning the weights
[8] Mahmood, D., Javaid, N., Ahmed, S., Ahmed, I., Niaz, I. A., Abdul, W., & Ghouzali, S. (2017). Orchestrating an effective formulation to investigate the impact of EMSs (energy
management systems) for residential units prior to installation. Energies, 10(3), 1-25.
18
Max_gen: Maximum Generation
Press_par: Particles’ Pressure
cr_rate: Crossover rate
mt_rate: Mutation rate
Max_vel: Maximum velocity
t: Time counter (T=24 hrs)
h: Counter for homes (H=total Homes(50))
p: Population size (p_size) counter (P=total Population)
rand_Pos: random positions
rand_vel: random velocity
Initial_Sol; Initial best Solution
cr_op: crossover operation
mt_op: mutation operation
best_Sol: final best solution
EC: Energy Consumption (max. allowed power)
App_PB: Appliance Priority Bit (0,1)
new_Pop: New Population
p_size: Population size
Proposed Algorithm
A New GWD Optimization Algorithm for DSM (1/5)
Time Complexity: O(n4+2log n)
Proposed Solution 2
Efficient Power Scheduling in Smart Homes
Using Jaya Based Optimization
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Samuel, O., Javaid, S., Javaid, N., Ahmed, S., H., Afzal, M., K., and Ishmanov, F. (2018). An Efficient Power
Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes.
Energies, 11(11), 1-27. [IF= 2.676].
Challenges Addressed:
Energy optimization using RESs
Effects of RESs integration on consumers’ cost
20
Efficient Power Scheduling in Smart Homes Using Jaya (1/4)
Fig. 6: System Model 2.
Appliance
class Appliances Power
rating
(kWh)
Starting time
(hours) Ending time
(hours) LOT (hours)
Shiftable
Cloth Dryer 1.5 06 14 02
Vacuum Cleaner 1 06 15 30 min
Heater 1.5 03 15 03
AC 1 12 24 10
Dish Washer 1 08 22 30 min
Pool Pump 2 12 21 08
EV 2.5 16 24 2.5
Television 0.25 01 16 6 hours 45 min
Iron 1 06 16 30 min
Hair Dryer 1 06 13 1 hour 30 min
Water Heater 1.5 06 23 03
Non-
Shiftable
Light 0.5 16 24 6 hours 15 min
Electric Stove 1.5 06 14 05
Personal
Computer 0.25 08 24 04
Refrigerator 0.125 06 06 24
Table 4: Appliance Classification.
Proposed System Model
RESs and Storage systems
are use for optimization
21
O1  
Constraints:
 if 
if  (1a)
1 
1 
O2  
O3  

=|

| (3a)
where


scheduled time by the scheduler
Problem Formulation and Flow
Chart of Proposed Solution
Fig. 7: Work Flow of Earliglow Algorithm.
Efficient Power Scheduling in Smart Homes Using Jaya (1/4)
Outline 22
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 2: Appliance Scheduling based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Simulation Results
Proposed Solution 1: A new GWD Algorithm for
DSM
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
24
A new GWD Algorithm for DSM (1/5)
Class
Name Appliances Power rating
(kWh) LOT
(hours) Deferrable / Non-
deferrable
Class A
Fan 0.5 11 0
Furnace Fan 0.38 8 0
Central AC 2.80 12 0
First-Refrigerator 0.50 24 0
Class B
Room AC 0.90 5 1
Space Heater 1.0 9 1
Heat Pump 0.11 4 1
Portable Heater 1.00 5 1
Water Heater 4.50 8 1
Clothes Washer 0.51 3 1
Clothes Dryer 5.00 2 1
Dishwasher 1.20 2 1
Table 5: Appliance power rating and operating intervals
Simulations are performed
in two cases:
For single home
For multi homes
Results and Discussions
Same simulation setup is
followed for both cases
25
Fig. 9: Cost obtained by GA, WDO and GWD.
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Energy Consumption and Cost of Single Home
A new GWD Algorithm for DSM (2/5)
Fig. 8: Energy consumption obtained by GA, WDO and GWD.
26
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Energy Consumption and Cost of Fifty Homes
A new GWD Algorithm for DSM (4/5)
Fig. 11: Cost obtained by GA, WDO and GWD.
Fig. 10: Energy consumption obtained by GA, WDO and GWD.
27
Fig. 16: UC and PAR.
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
PAR and UC of Single Home
A new GWD Algorithm for DSM (3/5)
Fig. 13: UC obtained by GA, WDO and GWD.
Fig. 12: PAR obtained by GA, WDO and GWD.
28
Fig. 18: Energy consumption and cost for fifty homes.
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
PAR and UC of Fifty Homes
A new GWD Algorithm for DSM (5/5)
Fig. 15: UC obtained by GA, WDO and GWD.
Fig. 14: PAR obtained by GA, WDO and GWD.
Simulation Results
Proposed Solution 2: Efficient Power Scheduling in
Smart Homes Using Jaya Based Optimization
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
30
Efficient Power Scheduling in Smart Homes Using
Jaya Based Optimization (1/3)
Simulations are performed
in three cases:
i. Without HEM
ii. With HEM
iii. HEM with RES
iv. List of appliances and
relevant details are
taken from Table 4.
For price tariff: ToU and
CPP
Results and
Discussions
Fig. 16: Using CPP. Fig. 17: Using ToU.
Fig. 18: Using CPP. Fig. 19: Using ToU.
31
Efficient Power Scheduling in Smart Homes Using
Jaya Based Optimization (2/3)
Fig. 20: Using CPP. Fig. 21: Using ToU.
Fig. 22: Using CPP. Fig. 23: Using ToU.
32
Efficient Power Scheduling in Smart Homes Using
Jaya Based Optimization (3/3)
PAR and UC
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Fig. 24: PAR obtained by using CPP and TOU. Fig. 25: Waiting time obtained by using ToU.
Outline 33
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 2: Appliance Scheduling based on RESs
Solution 3: World-Wide Adoptive Thermostat
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Proposed Solution 3
Energy Management With A Worldwide
Adaptive Thermostat Using FIS
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Javaid, S., Javaid , N., Iqbal, S., Mughal, M., J., (2017). Controlling energy consumption with the world-wide
adaptive thermostat using fuzzy inference system in smart grid, in 8th International Conference on Information
and Communication Technology (ICTC), 2017, South Korea, pp: 66-71.
Javaid, S., Javaid, N., Iqbal, S., Guizani, M., Almogren, A., and Alamri, A. (2018). Energy Management with a
World-wide Adaptive Thermostat using Fuzzy Inference System, IEEE Access, 6, pp.33489-33502, ISSN: 2169-3536.
[IF= 4.09]
Challenges Addressed:
Energy consumption of the HVAC system on the world-wide scale
Extending the country oriented thermostat
35
Energy Management With A Worldwide Adaptive Thermostat
Using FIS (1/4)
Fig. 26: System Model 3.
Proposed System Model
Energy consumption is
analysed based on the
outdoor and indoor
temperature
36
Energy Management With A Worldwide Adaptive Thermostat
Using FIS (2/4)
Objectives
O1   
Constraint
Minimization (Energy Consumption)
Computed via fuzzy rules, i.e.,
IF Tempoutdoor is “Normal” AND Pricerate is “Mid Peak” AND HO is “Present” AND I_SP is “Medium”, THEN
energy_consumption is “Medium”.
O2  
O3   
 EE
E 
[17] http://mcensustainableenergy.pbworks.com/w/page/20638179/Stationary%20Combustion%20Systems#ThermodynamicsandCombustionReview, [available online], accessed on:
21 July 2018.
Problem Formulation
37
Energy Management With A Worldwide Adaptive Thermostat
Using FIS (3/4)
IF Tempoutdoor is “Normal” AND Pricerate is “Mid Peak”
AND HO is “Present” AND I_SP is “Medium”, THEN
energy_consumption is “Medium”.
Table 6: List of Fuzzy Rules.
Temperature
Price
Occupancy
I_S
Ps
Energy Consumption
Hot
Cities
Cold
Cities
Hot
Cities
Cold
Cities
N
VC
OP
A
L
VL
VL
N
VC
OP
A
M
L
L
N
VC
OP
A
H
M
M
H
C
MP
A
H
M
H
H
C
MP
P
L
L
L
H
C
MP
P
M
M
M
VH
N
HP
P
L
L
VL
VH
N
HP
P
M
H
L
VH
N
HP
P
H
H
M
Fig. 27: Fuzzification of Input Variables.
38
Energy Management With A Worldwide Adaptive Thermostat
Using FIS (4/4)
Algorithm 1
Minimize energy consumption (Min_Econs(A)).
1: Initialization: A ← (Tempoutdoor, I_SPs, Prate and HO)
2: Fuzzification
3: Tempoutdoor ← {L, M, H}
4: I_SPs ← {L, M, H}
5: Prate ← {OP, MP, HP}
6: HO ← {A, P}
7: Rule Base by incorporating the fuzzy rules as in Table 5
8: FIS evaluation using Mamdani or Sugeno
9: for thr = 1 to Thr do
10: I_SPs = I_SPshr
11: if Normal_scenario then
12: if I_SPs(thr) == HP(thr)then
13: A [I_SPs] = I_SPs RedL
14: else if I_SPs(thr) == MP(thr)then
15: A [I_SPs] =I_SPs RedM
16: else
17: A [I_SPs] = I_SPs RedL
18: end if
19: end if
20: end if
21: else
22: def_Setpoint_CS(tCRD, RedL, RedM, RedH)
23: end if
24: Defuzzification
25: Using the aggregated Frules
26: Calculate Econs using Frules
27: Calculate Ecost and PAR
28: end
Time Complexity: O(n3+3nlogn)
Criteria for
load
reduction
Algorithm 2
Adjustment of setpoints for minimum energy consumption
(Min_Econs_Setpoints(A))
1: def_Setpoint_CS(tCRD, RedL, RedM, RedH)
2: if I_SPs(thr)==HP(thr) then
3: tCRD =3k *RedH
4: if I_SPs(thr)==MP(thr) then
5: tCRD =2k *RedM
6: else
7: tCRD =k *RedL
8: A [I_SPs]= tCRD
9: end if
10: end if
Time Complexity: O(3nlogn)
Criteria
for load
reduction
Proposed Solution 4
Comfort Evaluation of Seasonally and Daily
used Residential Load
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Javaid, S., Javaid, N., Iqbal, S., Aslam, S., and Rahim, M., H. (2018). Optimizing energy consumption of air-
conditioning systems with the fuzzy logic controllers in residential buildings, in 2018 International Conference on
Computing, Mathematics and Engineering Technologies (iCoMET), 2018.
Javaid, S., Javaid, N. (2020). Comfort evaluation of seasonally and daily used residential load in smart buildings for
hottest areas via predictive mean vote method. Sustainable Computing: Informatics and Systems, 25, 1-13. [IF=1.8]
Challenges Addressed:
Comfort index evaluation using the PMV
Comfort evaluation with the integration of RESs
40
Fig. 28: Proposed System Model 4.
Comfort Evaluation of Seasonally and Daily used
Residential Load via Predictive Mean Vote Method (1/4)
Proposed System Model (1/2)
Table 7: Occupants’ Data.
Inputs Energy management
module Control
actions
Load demand BPSO for load
scheduling Towards grid
RT Fuzzy logic for load
curtailment Towards
RESs
Tempoutdoor Sensor nodes Towards users
I_SPs Fuzzy logic Towards users
Pricerate Fuzzy logic Towards
utility
HO Sensor nodes Towards users
Table 8: Execution Phases of EMCs.
Parameters Values
Total apartments in building 10
Residents 3
Type of apartment Single family
Urban district New estate, rich
Energy Consumption per hour Medium
Uses
Have
41
Comfort Evaluation of Seasonally and Daily used
Residential Load via Predictive Mean Vote Method (2/4)
Prh, Prl: High\low priority
Decnh, Decnl: Decision for high\low priority
Occp, Occa: Occupancy present\absent
ECh, Ecm, ECl: Energy consumption high\ medium\ low
Comforth, Comfortm, Comfortl: Comfort high\ medium\ low
Proposed System Model (2/2)
TABLE 10: Appliances used in System.
Sr.
No. Appliances Power rating
(kWh) LOT Pre-
emptive
1 Space heater 1.00 9 1
2 Heat Pump 0.11 4 1
3 Portable Heater 1.00 5 0
4 Fan 0.50 11 0
5 Furnace Fan 0.38 8 1
6 Water Heater 4.50 8 1
7 Central AC 2.80 12 1
8 Room AC 3.51 10 1
9 Cloth Washer 0.51 9 1
10 Cloth Dryer 5.00 5 1
11 Dishwasher 1.20 11 1
12 First Refrigerator 0.50 24 0
13 Evap Cooling 0.40 1 1
14 Second Refrigerator 0.50 24 0
15 Freezer 0.29 24 0
16 Indoor Lighting 0.36 5 0
17 Range Burner 0.40 6 0
18 Television 0.20 3 0
19 Microwave 0.25 2 0
20 Personal Computer 0.35 4 0
21 Well Pump 0.90 2 1
TABLE 9: Rules for Appliance Usage Comfort.
Priority Control
Variable Occupancy Energy
Consumption Comfort
PrhDecnhOccpEChComforth
PrhDecnhOccaECmComfortm
PrhDecnlOccpECmComfortm
PrhDecnlOccaEClComfortl
PrlDecnhOccpECmComfortm
PrlDecnhOccaEClComfortl
PrlDecnlOccpEClComfortl
PrlDecnlOccaEClComfortl
42
Thermal Comfort [18]
  
 
 



 
 (1)
Appliance Usage Comfort [18]
  
Occp(2)
Comfort Evaluation of Seasonally and Daily used
Residential Load via Predictive Mean Vote Method (3/4)
Problem Formulation (2/2)
[18] Lu, C. H., Wu, C. L., Weng, M. Y., Chen, W. C., & Fu, L. C. (2017). Context-aware energy saving system with multiple comfort-constrained optimization in
M2M-based home environment. IEEE Transactions on Automation Science and Engineering,14(3), 1400-1414.
: Constant for normalization
: Represents thermal Comfort
Every activity I
: K number of activities (total)
 : Metabolic rate of each activity
: Appliance usage comfort
Scalefactor: 3 (Normalizing value)
Using metabolic rate, number of activities and constant factors
Dependent on the decision variable, occupancy level and priority of appliances
43
Comfort Evaluation of Seasonally and Daily used Residential
Load via Predictive Mean Vote Method (4/4)
Fig. 29: Membership functions for the OT in hot cities. Fig. 30: Membership functions for HO.
Fig. 31: Membership functions for the RT. Fig. 32: Membership functions for price.
Fig. 33: Membership functions for ISPs. Fig. 34: Membership functions for the EC.
Outline 44
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 2: Appliance Scheduling based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Simulation Results
Proposed Solution 3: Energy Management With A
Worldwide Adaptive Thermostat Using FIS
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
46
Energy Management With A Worldwide Adaptive Thermostat
Using FIS (1/2)
Table 11: Schedules of price rates and
specifications [10].
Time
intervals
P
rate ($)
Specification
12am
-07am
0.072
OPHs
07am
-11am
0.129
PHs
11am
-17pm
0.109
MPHs
17pm
-19pm
0.129
PHs
19pm
-24pm
0.072
OPHs
Results and Discussions
Two regions are considered:
Hot and cold regions
Performance parameters
Energy Consumption, Cost , PAR and
Energy Efficiency
Fig. 35: Energy consumption in hot cities over a complete day. Fig. 36: Cost of energy in hot cities over a complete day.
Fig. 37: PAR obtained for a complete day.
[10] Keshtkar, A., Arzanpour, S., Keshtkar, F., and Ahmadi, P. (2015). Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives. Energy and buildings, 104,
165-180.
47
Energy Management With A Worldwide Adaptive Thermostat
Using FIS (2/2)
Fig. 38: Energy consumption in cold cities over a complete day. Fig. 39: Cost of energy in cold cities over a complete day.
Fig. 40: PAR obtained for a complete day. Fig. 41: Energy efficiency of both cities.
Simulation Results
Proposed Solution 4: Comfort Evaluation of
Seasonally and Daily used Residential Load
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
49
Comfort Evaluation of Seasonally and Daily used Residential Load
via Predictive Mean Vote Method
Results and Discussions
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Fig. 42: Thermal and appliance usage comfort using
BPSOFMAM and RES. Fig. 43: Thermal and appliance usage comfort using BPSOFSUG
and RES.
Proposed Solution 5
Resource Allocation in Residential Buildings
Using C2F2C Based Framework
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Javaid, S., Javaid, N., Tayyaba, S., K., Abdul, N., S., Ruqia, B., and Zahid, M. (2018). Resource Allocation using
Fog-2-Cloud based Environment for Smart Buildings, in 14th IEEE International Wireless Communications and
Mobile Computing Conference (IWCMC) (pp. 1173-1177). IEEE.
Javaid, S., Javaid, N., Saba, T., Wadud, Z., Rehman, A., and Haseeb, A. (2019). Intelligent resource allocation in
residential buildings using consumer to fog to cloud based framework. Energies, 12(5), 1-23. [IF=2.707].
Challenges Addressed:
Integration of fog to cloud
Minimizing the delay
Analysing the PT and RT
51
Resource Allocation in Residential Buildings Using C2F2C Based
Framework (1/3)
Fig. 44: C2F2C Framework.
Proposed System Model
C2F2C: Consumer to Fog to
Cloud
All buildings comprises of
set of consumers, which
make requests for load
Fogs facilitate the
consumers, which make
requests for load
Cloud fulfils he fog’
resources for entertaining
consumers needs
52
Problem Formulation
Objectives [19]
O1Minimize 

makesspanNumber of the VMs 
O2Minimize 
  


  
O3= Minimize(Costtot 
Costtot CostDatatrans CostVM CostMG 
[19] Devi, D. C., & Uthariaraj, V. R. (2016). Load balancing in cloud computing environment using improved weighted round robin algorithm for non-preemptive dependent
tasks. The scientific world journal,2016.
Resource Allocation in Residential Buildings Using C2F2C Based
Framework (2/3)
: Completion time
: Request per hour
: Processing time
Costtot: Total cost
CostDatatrans: Data transfer cost
CostVM: VM cost
CostMG: MG cost
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
53
SJF Algorithm
Low latency
Resource Allocation in Residential Buildings Using C2F2C Based
Framework (3/3)
Table 12: Input parameters for both scenarios.
Parameters
Values
Number of VMs
25, 50
Executable task size
250 bytes
Users grouping factors
1000
Number of DCs
1, 2
DC processor speed
100 MIPS
VM image size
10,000 GB
DC available BW per machine
10,000 bps
DC storage per machine
100,000 Mb
DC memory per machine
2048 Mb
DC processors per machine
4
DC architecture
X86
VM memory
1024 Mb
VM BW
1000 Mb
DC VMM
Xen
DC VM policy
Time shared
Request grouping factors
100
1: Input: List of the incoming requests, List of the VMs
2: Output: Processing_Time, Response_Time, Cost
3: Initialization: maxCount = maxVal, VM_ID = 1
4: for VM=1, VM length(VM_list), VM++ do
5: if length(taski+1)<length(taski) then
6: Add taski+1 infront of the task queue
7: end if
8: if length(taskVM) == 0 then
9: Upgrade VM task count
10: end if
11: Return VM_ID
12: Calculate Response_Time
13: Calculate Request_Time
14: Calculate Processing_Time
15: Calculate Cost
16: end for
Time Complexity: O(n2)
SJF: Shortest Job First Algorithm
Outline 54
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 2: Appliance Scheduling based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Simulation Results
Proposed Solution 5: Resource Allocation in
Residential Buildings Using C2F2C Based
Framework
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
56
Results for Scenario 1 (case 1): With 25-VMs
Resource Allocation in Residential Buildings Using C2F2C
Based Framework (1/4)
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Fig. 45: Average Response Time by all fogs. Fig. 46: Average Processing Time by all fogs. Fig. 47: Total cost of VMs, MGs and Data Transfer.
57
Results for Scenario 1 (case 2): With 50-VMs
Resource Allocation in Residential Buildings Using C2F2C
Based Framework (2/4)
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Fig. 48: Average Response Time by all fogs. Fig. 49: Average Processing Time by all fogs. Fig. 50: Total cost of VMs, MGs and Data Transfer.
58
Results for Scenario 2 (case 1): With 25-VMs
Resource Allocation in Residential Buildings Using C2F2C
Based Framework (3/4)
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Fig. 51: Average Response Time by all fogs. Fig. 52: Average Processing Time by all fogs. Fig. 53: Total cost of VMs, MGs and Data Transfer.
59
Results for Scenario 2 (case 2): With 50-VMs
Resource Allocation in Residential Buildings Using C2F2C
Based Framework (4/4)
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Fig. 54: Average Response Time by all fogs. Fig. 55: Average Processing Time by all fogs. Fig. 56: Total cost of VMs, MGs and Data Transfer.
Outline 60
Introduction
Literature Review
Focused Problem
Proposed Solutions
Solution 1: GWD Algorithm for DSM
Solution 2: Appliance Scheduling based on RESs
Solution 2: World-Wide Adoptive Thermostat using FISs
Solution 4: Comfort Evaluation using PMV
Solution 5: C2F2C Framework
Simulation Results
Conclusion
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
61
Conclusions
We have observed that due to load curtailment, UC has been sacrificed
Integration of RESs instead of other fossil fuel resource,
Optimized the cost, energy consumption and user comfort
Using the single objective and multi-objective optimization methods
Analyzed the effect of heuristic optimization techniques for smart homes and
buildings
Integration of Cloud and Fog technologies with SG helps in fulfilling the on-
demand requests of residential consumers
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
62
Tackling of the black swarn occurrences
RESs contribution for the optimization of the black swarn occurrences
Zero-energy buildings will be the focus of our future work
Net energy consumption, meaning the total amount of energy used by the building on an annual
basis is roughly equal to the amount of renewable energy
Use of energy-efficient devices are also recommended; however, this direction requires
huge initial installation cost
Future Work
Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
63
References (1/3)
[1] Energy Information Administration https://www.eia.gov/todayinenergy /detail.cfm?id=12251 United States Department of
Energy, Washington (last visited 17th December 2015).
[2] International Energy Outlook 2017, https://www.eia.gov/outlooks/ieo/pdf/0484 (2017). (last visited 29th March 2019).
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[4] What Is Green Power. https://www.epa.gov/greenpower/what-green-power. (last visited 29th March 2019).
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10(3), 335.
[8] Ogunjuyigbe, A. S. O., Ayodele, T. R., & Akinola, O. A. (2017). User satisfaction-induced demand side load management in
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64
References (2/3)
[9] Keshtkar, A., Arzanpour, S., Keshtkar, F., and Ahmadi, P. (2015). Smart residential load reduction via fuzzy logic, wireless sensors,
and smart grid incentives. Energy and buildings, 104,165-180.
[10] Keshtkar, A., and Arzanpour, S. (2017). An adaptive fuzzy logic system for residential energy management in smart grid
environments. Applied Energy, 186,68-81.
[11 Li, M., Li, G. Y., Chen, H. R., & Jiang, C. W. (2018). QoE-aware smart home energy management considering renewables and
electric vehicles. Energies, 11(9), 1-16.
[12] Fayaz, M., & Kim, D. (2018). Energy consumption optimization and user comfort management in residential buildings using a bat
algorithm and fuzzy logic. Energies, 11(1), 1-22.
[13] Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., & Wang, X. (2017). Optimal cloud computing resource allocation for demand side
management in smart grid. IEEE Transactions on Smart Grid, 8(4), 1943-1955.
[14] Yaghmaee , M. H., Leon-Garcia , A., Moghaddassian, M. (2016). On the performance of distributed and cloud-based demand
response in smart grid. IEEE Transactions on Smart Grid, doi:10.1109/TSG.2017.2688486.
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scheduling. IEEE Transactions on Industrial Informatics, 13(6), 3312-3321.
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areas via predictive mean vote method. Sustainable Computing: Informatics and Systems, 25, 1-13. [IF=1.8].
[10] Javaid, S., Javaid, N., Saba, T., Wadud, Z., Rehman, A., and Haseeb, A. (2019). Intelligent Resource Allocation in Residential
Buildings using Consumer to Fog to Cloud based Framework. Energies, 1-22. [IF= 2.676].
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Adaptive Thermostat using Fuzzy Inference System, IEEE Access, 33489-33502. [IF= 3.745].
[8] Samuel, O., Javaid, S., Javaid, N., Ahmed, S., H., Afzal, M., K., and Ishmanov, F. (2018). An Efficient Power Scheduling in Smart
Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes. Energies, 11(11), 1-27. [IF=2.676].
[7] Jamil, A., Alghamdi, T. A., Khan, Z. A., Javaid, S., Haseeb, A., Wadud, Z., and Javaid, N. (2019). An Innovative Home Energy
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[5] Bukhsh, R., Javaid, N., Javaid, S., Ilahi, M. and Fatima, I. (2019), "Efficient Resource Allocation for Consumers Power Requests in
Cloud-Fog based System", International Journal of Web and Grid Services, ISSN: 1741-1114. [IF=1.071].
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energy management system in smart grid. Journal of Ambient Intelligence and Humanized Computing, 1-17. [IF=1.91].
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Efficient Resource Management. Sustainability, 10(6), 1-21. [IF=2.075]. Download.
[1] Javaid, N., Javaid, S., S., Abdul, W., Ahmed, I., A., Almari, A. and Niaz, I., A. (2017). A hyrid genetic wind driven heuristic
optimization algorithm for demand side management in smart grid, Energies, 10(3), pp: 1-27. [IF=2.676].
Publications (2/6) [Total IF=28.751]
Journal Publications (2/2)
68
[25] Khan, M. A., Javaid, N., Javaid, S., Khalid, A., Nasser, N., and Imran, M. (2020, June). A novel cooperative link selection
mechanism for enhancing the robustness in scale-free IoT networks. In 2020 International Wireless Communications and Mobile
Computing (IWCMC) (pp. 2222-2227). IEEE.
[24] Khalid, R., Javaid, N., Javaid, S., Imran, M., and Naseer, N. (2020, June). A blockchain-based decentralized energy
management in a P2P trading system. In ICC 2020-2020 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
[23] Khan, S., Khan, Z. A., Noshad, Z., Javaid, S., and Javaid, N. (2019, November). Short Term Load and Price Forecasting using
Tuned Parameters for K-Nearest Neighbors. In 2019 Sixth HCT Information Technology Trends (ITT) (pp. 89-93). IEEE.
[22] Iftikhar, M. Z., Javaid, N., Javaid, S., Imran, M., and Nasser, N. (2020, June). TFPMS: Transactions Filtering Pattern Matching
Scheme for Vehicular Networks based on Blockchain. In 2020 International Wireless Communications and Mobile Computing
(IWCMC) (pp. 2128-2132). IEEE.
[21] Arshad, U., Javaid, S., Ahmed, S., Seemab, B. and Javaid, N. (2019), A Futuristic Blockchain based Vehicular Network
Architecture and Trust Management System. In International Conference on Advances in the Emerging Computing Technologies
(AECT). (Accepted).
[20] Mujeeb, S., Javaid, N., and Javaid, S. (2018, November). Data analytics for price forecasting in smart grids: a survey. In 2018
IEEE 21st International Multi-Topic Conference (INMIC) (pp. 1-10). IEEE.
[19] Rehman, U. O., Khan, S., A., Malik, M., Javaid, N., Javaid, S. and Aurangzeb, K. (2018). Optimal scheduling of distributed
energy resources for load balancing and user comfort management in smart grid. In the 2018 International Conference on Innovation
and Intelligence for Informatics, Computing and Technologies (3ICT 2018). (Accepted).
Publications (3/6)
Conference Publications (1/4)
69
[18] Usman, M., Khan, Z. A., Khan, I. U., Javaid, S., and Javaid, N. (2019, November). Data Analytics for Short Term Price and
Load Forecasting in Smart Grids using Enhanced Recurrent Neural Network. In 2019 Sixth HCT Information Technology Trends
(ITT) (pp. 84-88). IEEE.
[17] Butt, A. A., Khan, S., Ashfaq, T., Javaid, S., Sattar, N. A., and Javaid, N. (2019, June). A cloud and fog based architecture for
energy management of smart city by using meta-heuristic techniques. In 2019 15th International Wireless Communications &
Mobile Computing Conference (IWCMC) (pp. 1588-1593). IEEE.
[16] Hameed, S., Javaid, S., Ahmed, S., and Javaid, N. (2019). Sharing Mechanism of Intelligent Vehicles Trust Points based on
Blockchain for Vehicular Networks. In International Conference on Advances in the Emerging Computing Technologies (AECT).
(Accepted).
[15] Shafiq, S., Javaid, N., Javaid, S. and Aurangzeb, K. (2018). Optimal power flow problem with the integrated renewable energy
sources: A survey. In the 2018 International Conference on Innovation and Intelligence for Informatics, Computing and
Technologies (3ICT 2018). (Accepted).
[14] Mujeeb, S., Javaid, N., Javaid, S., Rafique, A., and Ilahi, M. (2018). Big Data Analytics for Load Forecasting in Smart Grids: A
Survey. International Conference on Cyber Security and Computer Science (ICONCS) (pp. 193-202).
[13]Naz, A., Javaid, N., and Javaid, S. (2018, November). Enhanced Recurrent Extreme Learning Machine Using Gray Wolf
Optimization for Load Forecasting. In 2018 IEEE.21st International Multi-Topic Conference (INMIC) (pp. 1-5). IEEE.
[12] Javaid, S., Javaid, N., Iqbal, S., Aslam, S., and Rahim, M., H. (2018, March). Optimizing energy consumption of air-
conditioning systems with the fuzzy logic controllers in residential buildings, in 2018 International Conference on Computing,
Mathematics and Engineering Technologies (iCoMET), 2018.
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[11] Javaid, S., Javaid, N., Tayyaba, S. K., Sattar, N. A., Ruqia, B., and Zahid, M. (2018, June). Resource allocation using Fog-2-
Cloud based environment for smart buildings. In 2018 14th InternationalWireless Communications & Mobile Computing
Conference (IWCMC) (pp. 1173-1177). IEEE.
[10] Javaid, S., Javaid, N., Iqbal, S., and Mughal, M. J. (2017, October). Controlling energy consumption with the world-wide
adaptive thermostat using fuzzy inference systemin smart grid. In 2017 International Conference on Information and
Communication Technology Convergence (ICTC) (pp. 66-71). IEEE.
[9] Javaid, S., Javaid, N., S., Javaid, S., Qasim, U., and Khan, Z., A. (2016, July). Optimal scheduling in smart homes with energy
storage using appliances’ super-clustering, In Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 1-6.
[8] Javaid, S., Abdullah, M., Javaid, N., Sultana, T., Ahmed, J., and Sattar, N. A. (2019, June). Towards Buildings Energy
Management: Using Seasonal Schedules Under Time of Use Pricing Tariff via Deep Neuro-Fuzzy Optimizer. In 2019 15th
International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 1594-1599). IEEE.
[7] Fatima, I., Javaid, S., Javaid, N., Shafi, I., Nadeem, Z., and Ullah, R. (2018, July). Region Oriented Integrated Fog and Cloud
Based Environment for Efficient Resource Distribution in Smart Buildings. In Conference on Complex, Intelligent, and Software
Intensive Systems (pp. 749-759). Springer, Cham.
[6] Aslam, S., Javaid, S., Javaid, N., Mohsin, S. M., Khan, S. S., and Akbar, M. (2018, July). An Efficient Home Energy
Management and Power Trading in Smart Grid. In International Conference on Innovative Mobile and Internet Services in
Ubiquitous Computing (pp. 231-241). Springer, Cham.
Publications (5/6)
Conference Publications (3/4)
71
[5] Khan, A., Javaid, N., and Javaid, S. (2018, November). Optimum unit sizing of stand-alone PV-WT-Battery hybrid system
components using Jaya. In 2018 IEEE 21st International Multi-Topic Conference (INMIC) (pp. 1-8). IEEE.
[4] Feroze, F., Khan, A., Qayyum, N., Javaid, S., Ahmed, A., Rahim, M. H., and Javaid, N. (2017, November). A Survey of
Optimization Techniques for Scheduling in Home Energy Management Systems in Smart Grid. In International Conference on
Broadband and Wireless Computing, Communication and Applications (pp. 616-626), Springer, Cham.
[3] Pamir, Javaid, S., Ali, I., Mushtaq, N., Faiz, Z., Sadiq, H. A., and Javaid, N. (2017, November). Enhanced Differential Evolution
and Crow Search Algorithm Based Home Energy Management in Smart Grid. In International Conference on Broadband and
Wireless Computing, Communication and Applications (pp. 73-86). Springer, Cham.
[2] Abbasi, B. Z., Javaid, S., Bibi, S., Khan, M., Malik, M. N., Butt, A. A., and Javaid, N. (2017, November). Demand Side
Management in Smart Grid by Using Flower Pollination Algorithm and Genetic Algorithm. In International Conference on P2P,
Parallel, Grid, Cloud and Internet Computing (pp. 424-436). Springer, Cham.
[1] Abid, S., Zafar, A., Khalid, R., Javaid, S., Qasim, U., Khan, Z. A., and Javaid, N. (2017, July). Managing Energy in Smart Homes
Using Binary Particle Swarm Optimization. In Conference on Complex, Intelligent, and Software Intensive Systems, pp.189-196.
Springer, Cham.
Publications (6/6)
Conference Publications (4/4)
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Towards Green World: Renewable Energy Source based Energy Management in Residential
Sector making Appliances, Homes and Buildings Smart
PhD Thesis Defense by Sakeena Javaid, August, 11, 2020
Thanks for patient hearing
Questions and Answers
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