This document summarizes a thesis on implementing an optimized smart home energy management system using IoT applications and the PSO optimization algorithm. It describes a smart plug that monitors and controls appliances remotely, a Raspberry Pi-based energy management controller (EMC) that schedules appliances using MQTT, and a mobile app for remote monitoring and control. Experimental results found that using PSO to schedule appliances based on time-of-use pricing achieved a 24.31% reduction in energy costs compared to other methods. The conclusions discuss using smart plugs and the EMC to accurately read appliance consumption data and schedule appliances optimally via MQTT to reduce user costs.

1. REPUBLIC OF IRAQ
MINISTRY OF HIGHER EDUCATION AND SCIENTIFIC RESEARCH
MIDDLE TECHNICAL UNIVERSITY
ELECTRICAL ENGINEERING TECHNICAL COLLEGE
By
Rusul Hamdi Hussain
Supervised by
Assist. Prof. Dr. Mohamed Ibrahim Shujaa
IoT-Based Secure Energy Pricing Management Controller

2.  Introduction.
 Model of Smart Grid System.
 Problem statements.
 Objective.
 Real-Time Pricing scheme.
 Theoretical Background.
 Experiment Results and Discussion.
 Conclusions.
 List of papers.
Outlines

3.  The Home Energy Management System (HEMS), which incorporates a remote management network using network
technologies, helps consumers to smart home devices .
 HEMS consists of hardware and software that allows consumers to effectively handles energy usage through time-consuming
monitoring of intelligent home appliances. HEMS provides advantages in terms of a power schedule problem to optimize
electricity usage and reliability of energy systems.
Introduction

4. Model of Smart Grid System.
 Smart grid consists of providers of power supplier (data center), intelligent nodes, power
generators, data network, energy network, and smart buildings or homes.

5. Problem statements
 The current electrical networks, the complexity of the distribution network, the widening gap
between energy production and consumption, the ever-increasing demand for electrical energy with
an increasing population, global climate change, energy storage problems, and high initial cost of
renewable energy are the main factors that provide additional limitation on the ability to generate
additional electricity capacity . All these problems provide more incentive to balance the available
generation capacity with the energy demand at all times. Therefore, researchers give more attention
for DR of smart grid, for optimal load scheduling to achieve essential cost and energy saving.
 Reading the consumption data for the available household devices is a critical role in load
scheduling . Most of the current available work did not consider the accuracy of load
consumption data. Therefore, effective and accurate consumption data reading provide direct
connection with load management.
 Data communication is the foundation of the load management of smart grid. Home area networks
(HAN) provides effective connection with home load to achieve optimal load management. Thus,
scalable and interoperable HAN connection network can support the load scheduling program for
essential cost reduction.

6. Objective
The main objective of this thesis is to implement an optimized smart home energy
management system and IoT application systems with the most suitable optimization using
PSO
A. Designed a Smart plug that helps consumers to remote monitor and controls their electrical appliances.
B. Raspberry Pi has been used and programmed to become one of the main elements of EMC. It can send and
receives commands to a smart plug using the MQTT protocol according to the optimal scheduling time of
household appliances.
C. A mobile application has been built to allow consumers to remotely watch and control the electrical
appliances
D. A public server has been rented (MQTT server) that is used to connect smart plugs, mobile applications, and
EMC with each other.
The main objective is verified through the following sub-objectives:

7. Real-Time Pricing scheme

8. Experiment Results and Discussion
Pricing is used, with off-peak hours of 00:00 to 7:00, 11:00 to 18:00, and peak hours of 7:01 to 10:59
,18:01 to 22:00 being designated. Off-peak rates are 45.54 cents per kWh, whereas peak rates are
144.52 cents per kWh
 Cost rate during on-peak and off-peak

9. Experiment Results and Discussion
 Appliances Data
No. Devices Power rate (W) Duration
(slot/day)
Start (slot) End (slot)
1 Micro Wave 827 2 84 86
Micro Wave 827 2 240 242
2 Oven 1832 24 228 252
3 Vacuum 1296 12 156 168
4 Washing Machine 270 24 144 168
5 Air conditioner 2108 60 132 192
Air conditioner 2108 48 216 264
Air conditioner 2108 72 1 73
6 Water pump 403 24 192 216
7 Hair Dryer 1234 6 228 234
8 Oil Heater 1295 60 1 61

10. Experiment Results and Discussion
 Appropriate Time Tables for Home Appliances
No. Devices
Power rate
(W)
Duration
(slot/day)
normal
operating periods PSO
Start
(slot)
End
(slot)
Start
(slot)
End
(slot)
1 Micro Wave 827 2 84 86 82 84
Micro Wave 827 2 240 242 214 216
2 Oven 1832 24 228 252 200 224
3 Vacuum 1296 12 156 168 143 155
4 Washing Machine 270 24 144 168 136 160
5 Air conditioner 2108 60 132 192 155 215
Air conditioner 2108 48 216 264 185 233
Air conditioner 2108 72 1 73 1 73
6 Water pump 403 24 192 216 185 209
7 Hair Dryer 1234 6 228 234 208 214
8 Oil Heater 1295 60 1 61 3 63
PSO provides the best results in the shortest amount of time

11. 0
1000
2000
3000
4000
5000
6000
7000
0 25 50 75 100 125 150 175 200 225 250 275 300
Power
(W)
"Time (Slot)"
B. Load distribution using BFO
data before schedule BFO
Experiment Results and Discussion
Total load distribution For PFO

12. 0
1000
2000
3000
4000
5000
6000
0 25 50 75 100 125 150 175 200 225 250 275 300
Power
(W)
"Time (Slot)"
C. Load distribution using Am
data before schedule data Am
Experiment Results and Discussion
Total load distribution For AM

13. Experiment Results and Discussion
Total load distribution For PSO
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 25 50 75 100 125 150 175 200 225 250 275 300
Power
(W)
Time (Slot)
A. Load distribution using PSO
data before schedule PSO
PSO achieved a significant costs reduction of 24.31% compared to AM and BFO.

14. Experiment Results and Discussion
 Energy and cost results
DR
Scheme
Optimization
Method
Sampling time
(min.)
Total energy (kW
/h)
Peak consumption(kW) Total cost
(R)
Before scheduling - - - 14.308 5.021 11.53
ToU BFO 5 14.308 3.414 8.76
ToU PSO 5 14.308 3.414 8.99
ToU AM 5 14.308 3.414 8.87
PSO show the best results for total cost reductions

15. Hardware Design

16.  Message Queuing Telemetry Transport (MQTT)

17.  Details of cloud MQTT website.

18. Transfer data between MQTT server and smart plugs.

19. Message Queuing Telemetry Transport (MQTT)

20. Contents of the mobile and desktop applications
Message Queuing Telemetry Transport (MQTT)…Cont

21.  The proposed of using demand response is to control the energy consumption of individual households, which
helps to reduce the need for additional power plants to meet the growing demand. The utility companies
benefit from lower peak demand, and consumers benefit from lower energy bills. Thus, the percentage of
carbon dioxide released by power plants will be decreased, resulting in environmental protection.
 Homeowners can save money on their monthly electricity bills by using a variety of scheduling techniques to
plan when electrical equipment will be used. When compared to other optimization strategies, it has been
found that the suggested PSO method based on the ToU pricing scheme can reduce electricity costs by a
significant amount in a short period of time.
 PSO method was applied for consumption data. consumption data are collected using proposed smart plugs
for typical house (in Baghdad), the results found that PSO achieved a significant costs reduction of 24.31%
compared to AM and BFO.
Conclusions

22.  Smart plug helps consumer to remote monitoring and control the electrical appliances. In addition, the
possibility to read and store six different types of data representing voltage, current, power, energy, power
factor and frequencyhave been used in the proposed load scheduling algorithm.
 Raspberry Pi has been used and programmed to become one of the main elements of EMC. It can send
and receives commands to smart plug using theMQTT protocol according to optimal scheduling time of
household appliances.
 Mobile and desktop application has been built to allow consumers to remotely watch and control the
electrical appliances in real time and display their usage information, such as voltage, current and
appliances status (ON or OFF).
 A public server has been rented (MQTT server) that is used to connect smart plugs, mobile application,
and EMC with each other.
Conclusions…Cont

23. List of Papers
 Rusul H Hussain and Mohamed Ibrahim Shujaa “Data Consumption-Based Home Energy
Management for Residential Platform” AIP Conference Series: “3rd International Conference on
Smart Cities and Sustainable Planning (SCSP)”.
 Rusul H Hussain and Mohamed Ibrahim Shujaa “IoT Residential Appliance Monitoring and
Controlling System Using MQTT Protocol” AIP Conference Series: Fourth Scientific
Conference for Electrical Engineering Techniques Research (EETR 2022), Baghdad, Iraq on 8-9
June 2022 (Indexed by: Scopus).
 Rusul H Hussain and Mohamed Ibrahim Shujaa “Accuracy Data Consumption-Based for
Residential Platform” Journal of Techniques (MTU).

24. Thank you For
Attention