Solar Photovoltaic Maximum Power Point Tracking (MPPT) and the Integration of IoT for Enhanced Performance and Monitoring (www.kiu.ac.ug)

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Solar Photovoltaic Maximum Power Point Tracking (MPPT) and
the Integration of IoT for Enhanced Performance and Monitoring
Enuru Holland Simera
Department of Science and Technology, Kampala International University, Uganda
ABSTRACT
The demand for renewable energy, particularly solar energy, has surged as concerns about climate change and
environmental sustainability intensify. Solar photovoltaic (PV) systems play a central role in renewable energy
generation, but their efficiency is influenced by dynamic environmental factors such as sunlight intensity and
temperature. Maximum Power Point Tracking (MPPT) techniques are used to optimize power extraction under
varying conditions. With the advent of the Internet of Things (IoT), the operation and monitoring of solar PV
systems can be significantly enhanced. This paper explores the integration of IoT with MPPT techniques,
emphasizing how IoT technologies, including real-time monitoring, predictive maintenance, and data analytics, can
optimize solar PV system performance. The synergy between MPPT and IoT enhances the adaptability and
efficiency of solar systems, offering potential solutions for real-time optimization and remote diagnostics. Despite
challenges such as data security, cost, and connectivity, the integration of IoT with MPPT presents a promising
pathway for optimizing solar power generation.
Keywords: Solar PV, MPPT, IoT, renewable energy, optimization, real-time monitoring, energy management
INTRODUCTION
The global demand for renewable energy has grown
substantially in recent years, driven by the
intensifying concerns over climate change and the
need for environmental sustainability [1,2]. Among
the various renewable energy sources, solar energy
stands out as one of the most widely adopted
solutions, with Solar Photovoltaic (PV) systems
experiencing rapid growth in both residential and
commercial applications [3,4]. Solar PV systems are
particularly attractive due to their ability to convert
sunlight into electricity without producing harmful
emissions [5,6]. However, despite their widespread
use, the efficiency of these systems is heavily
influenced by several environmental factors,
including sunlight intensity, temperature, and the
angle at which sunlight strikes the solar panels [7].
These factors are not static; they vary throughout the
day and across seasons, resulting in fluctuations in the
power output of the system. To maximize the
efficiency of solar PV systems, it is essential to
optimize their power output continuously [8]. This
is where Maximum Power Point Tracking (MPPT)
techniques come into play. MPPT refers to a set of
algorithms designed to adjust the operating point of
a solar PV system in real-time, ensuring that the
system operates at its peak efficiency under varying
environmental conditions [9,10]. By constantly
tracking and adjusting the operating point, MPPT
maximizes the amount of energy extracted from the
system, thus enhancing its overall performance. In
recent years, the advancement of the Internet of
Things (IoT) technology has provided new
opportunities to further optimize the operation and
monitoring of solar PV systems. IoT technologies,
which connect physical devices and systems to the
internet, enable real-time data collection and analysis,
offering insights into the performance and efficiency
of solar systems [11,12]. By integrating IoT
solutions into solar PV systems, operators can
achieve enhanced capabilities such as real-time
monitoring, predictive maintenance, and performance
analytics [13,14]. These capabilities can significantly
improve the operation and longevity of solar systems,
ensuring that they continue to perform efficiently
INOSR Scientific Research 12(1):53-62, 2025. ISSN: 2705-1706
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throughout their lifecycle. This paper aims to explore
the relationship between MPPT techniques and IoT,
focusing on how IoT integration can optimize the
performance of solar PV systems [15,16,17].
Through the exploration of the potential synergy
between these two technologies, the paper seeks to
provide insights into the future of solar energy
systems, with an emphasis on improving efficiency,
reducing downtime, and ensuring the sustainability of
solar power generation.
Theoretical Background of Solar PV and MPPT
Solar photovoltaic (PV) technology plays a vital role
in renewable energy generation by converting
sunlight directly into electricity through the use of
semiconductor materials [18,19]. A solar panel's
ability to generate electricity depends on the intensity
of the sunlight it receives and the surrounding
temperature, both of which are highly dynamic and
subject to constant fluctuation throughout the day
[20,21]. The efficiency of a solar PV system is
therefore dependent on various environmental
factors. In order to maximize the system's efficiency,
it is important to understand how the performance of
the PV system can be mapped across a power-voltage
(P-V) curve [22,23]. This curve illustrates the
relationship between the output power and the
operating voltage of the system, with the maximum
power being achieved at a specific voltage point
known as the Maximum Power Point (MPP)
[24,25,26]. However, this point varies depending on
factors like time of day and weather conditions. As
such, continuously tracking and adjusting the
system's operating point is crucial for optimal
performance, which is where Maximum Power Point
Tracking (MPPT) algorithms come into play. MPPT
is a vital process for optimizing the performance of
PV systems, ensuring that the system operates at its
maximum potential under varying environmental
conditions. Since the MPP shifts due to changes in
sunlight, temperature, or other factors, MPPT
algorithms are used to adjust the system's operating
parameters in real-time [13,26]. Over time, several
techniques for MPPT have been developed, each with
its unique advantages and limitations. The most
commonly used MPPT techniques include:
1. Perturb and Observe (P&O): The Perturb
and Observe method is the most widely
adopted MPPT technique due to its
simplicity and ease of implementation. It
works by perturbing (slightly altering) the
operating voltage of the PV system and
observing the resulting change in power
output. Based on this observation, the
system adjusts the operating point to
maximize the power output. While P&O is
simple and effective, it can be less accurate
under rapidly changing environmental
conditions [27].
2. Incremental Conductance (IncCond): The
Incremental Conductance method is more
advanced than P&O and tracks the MPP by
comparing the incremental conductance of
the PV system to the instantaneous
conductance. This technique can more
accurately identify the MPP, especially when
environmental conditions are changing
rapidly, such as during partial shading or
fast cloud movement. As a result, IncCond
provides better performance in real-world,
dynamic conditions compared to P&O [28].
3. Artificial Neural Networks (ANNs):
Artificial Neural Networks (ANNs) are a
form of machine learning that can be
employed for MPPT. These algorithms use
historical and real-time data to predict the
MPP, thereby increasing the accuracy and
efficiency of power tracking. ANNs excel at
handling complex, non-linear relationships
in the data and can adapt to changing
conditions, making them suitable for
dynamic environments. However, their
implementation is more computationally
intensive compared to traditional methods
like P&O and IncCond, requiring greater
processing power and training data [29,30].
4. Fuzzy Logic Control (FLC): Fuzzy Logic
Control-based MPPT uses fuzzy rules to
simulate human decision-making in
adjusting the system's parameters. FLC is a
flexible approach that strikes a balance
between the simplicity of methods like P&O
and the accuracy of more complex
techniques like ANNs. It operates by
mapping the input values (e.g., voltage,
current) to output values through a set of
fuzzy logic rules, allowing it to adjust the
system's settings in a way that optimizes
performance while being relatively simple to
implement.
The MPPT is an essential component in maximizing
the efficiency of solar PV systems, and various
techniques have been developed to track the
maximum power point under different environmental
conditions [31,32,33]. While simpler methods like
P&O are widely used for their ease of implementation,
more advanced methods such as IncCond, ANNs, and

55
FLC offer increased accuracy and performance under
dynamic conditions, albeit at the cost of greater
complexity or computational requirements. The
choice of MPPT technique depends on factors such as
the specific application, environmental conditions,
and the desired balance between accuracy and
computational effort.
Internet of Things (IoT) and Solar PV Integration
The Internet of Things (IoT) has significantly
transformed numerous industries by enabling the
interconnection of physical devices through the
internet, allowing them to exchange data seamlessly
[34,35,36]. In the context of solar photovoltaic (PV)
systems, this interconnection provides a powerful tool
for continuous monitoring of critical system
parameters such as voltage, current, temperature, and
irradiance in real-time. By integrating IoT with solar
PV systems, operators can gain comprehensive
insights into system performance and optimize its
efficiency [37,38,39]. This integration offers several
notable benefits that enhance the functionality and
reliability of solar PV systems. One of the key
advantages of IoT integration in solar PV systems is
real-time monitoring and control [40]. With IoT-
based sensors installed in various components of the
PV system, parameters such as power output,
efficiency, and environmental conditions (e.g.,
temperature, irradiance) are tracked continuously.
This allows operators to monitor the system's
performance in real-time, enabling immediate
adjustments to optimize energy generation. If any
deviations from the optimal operating conditions are
detected, such as lower efficiency due to shading or
temperature fluctuations, corrective actions can be
taken promptly to restore peak performance. This
dynamic and responsive monitoring helps maintain
the PV system’s overall efficiency and reliability [41].
In addition to real-time monitoring, IoT also
facilitates remote diagnosis and predictive
maintenance. Faults or inefficiencies in the PV
system can be detected early through the data relayed
by IoT sensors to cloud-based platforms. Machine
learning algorithms process this data to predict
potential failures or performance degradation before
they become critical issues. By identifying patterns
and anomalies in the data, IoT-based systems can
proactively alert operators to necessary maintenance
or repairs. This not only helps in minimizing
downtime but also prevents costly repairs by
addressing minor issues before they escalate [42,43].
The ability to perform remote diagnosis also
eliminates the need for frequent physical inspections,
reducing maintenance costs and increasing
operational efficiency. Another significant benefit of
IoT integration is data analytics and performance
optimization [44,45]. The large volumes of data
generated by IoT systems can be analyzed to gain
valuable insights into the operation of the PV system.
For example, real-time data can be used to adjust the
settings of Maximum Power Point Tracking (MPPT)
controllers based on changing environmental
conditions, such as variations in sunlight intensity or
temperature [46,47]. By continuously fine-tuning
these settings, the system can operate at maximum
efficiency throughout the day, enhancing the overall
performance of the solar PV system. Additionally, the
data can help identify long-term performance trends,
enabling the implementation of strategies to further
optimize energy generation. Finally, IoT integration
supports energy management, a critical aspect of
solar PV systems, especially when they are part of a
larger grid or energy network. IoT-enabled systems
can monitor the energy generated by the PV system
and make real-time adjustments to how the energy is
distributed [48,49]. For example, IoT systems can
optimize energy storage by adjusting battery
charging based on the generation and consumption
patterns, ensuring that excess energy is stored for use
during periods of low sunlight. Additionally, IoT can
manage energy consumption in connected appliances,
adjusting their operation to maximize the use of solar
power and reduce reliance on the grid [50]. This
dynamic energy management ensures that the energy
produced is used optimally, leading to more
sustainable energy consumption and cost savings.
The integration of IoT with solar PV systems offers
a range of benefits, including real-time monitoring,
remote diagnostics, predictive maintenance, data
analytics for performance optimization, and efficient
energy management. These capabilities enable solar
PV systems to operate more efficiently, reduce
downtime, and provide greater control over energy
production and consumption [51]. As IoT
technology continues to evolve, its role in optimizing
solar PV systems will only grow, further enhancing
the sustainability and efficiency of renewable energy
solutions.
Synergy Between MPPT and IoT
The integration of Maximum Power Point Tracking
(MPPT) with the Internet of Things (IoT) offers a
revolutionary approach to enhancing the performance
and sustainability of solar photovoltaic (PV) systems.
Solar energy generation is inherently dynamic, as
factors such as sunlight intensity, temperature, and

56
weather conditions can fluctuate throughout the day
[52,53]. Consequently, MPPT is crucial to
continuously optimizing the power extraction
process to ensure that the system operates at its
maximum potential [54,55]. By incorporating IoT
technology, MPPT algorithms can be implemented in
real-time, allowing for continuous monitoring of
environmental conditions and system performance.
The synergy between MPPT and IoT facilitates
several key outcomes, greatly improving the
efficiency and flexibility of solar PV systems.
1. Enhanced Efficiency: One of the major
advantages of integrating IoT with MPPT
is the enhanced efficiency of the system. IoT
devices, such as sensors and smart meters,
provide continuous data streams on
environmental factors like temperature,
irradiance, and voltage, which can be used by
MPPT algorithms to adjust the operating
points of the solar system instantaneously
[56]. This ensures that the system is
constantly optimized for maximum power
extraction, even as environmental
conditions change. The real-time data
processing enabled by IoT helps eliminate
inefficiencies that may arise due to static
adjustments, enhancing overall energy
output.
2. Real-Time Adaptability: IoT significantly
improves the adaptability of solar PV
systems. By providing real-time feedback
from the system, IoT allows MPPT
controllers to dynamically adjust to
changing environmental conditions, such as
fluctuating irradiance or temperature [57].
For example, if a cloud passes over the solar
panels, the IoT system can promptly
transmit data about the sudden change in
irradiance, prompting the MPPT algorithm
to alter the operating point to maintain
optimal power extraction. This level of
adaptability ensures that the system is
always operating near the optimal power
point, even under variable conditions.
3. Optimized System Configuration: The
integration of IoT in solar PV systems
allows for more efficient and optimized
system configuration. IoT devices can be
used to monitor the performance of various
components within the solar system,
including inverters, batteries, and solar
panels. By collecting and analyzing data on
the status of each component, IoT systems
can facilitate remote adjustments to ensure
that every part of the system operates at
peak efficiency [58]. For instance, if the
battery is nearing full charge, IoT can
trigger the adjustment of the inverter’s
settings to avoid overcharging and ensure
the longevity of the battery.
4. Scalability and Integration: The use of IoT
also enhances the scalability and integration
of solar PV systems. As the number of solar
installations increases, managing and
monitoring individual systems becomes
increasingly complex. IoT allows for the
centralized monitoring and control of
multiple systems, making it easier to manage
large-scale solar farms or distributed solar
setups [59]. IoT networks enable operators
to observe the performance of all
components across various locations from a
central dashboard, enabling quicker
decision-making and reducing the need for
manual intervention. This scalability is
particularly valuable in areas where large-
scale solar installations are being deployed,
as it ensures seamless operation and
maintenance of the entire system.
The synergy between MPPT and IoT offers a
transformative solution for optimizing the
performance of solar PV systems. By enabling real-
time monitoring, adaptability, and efficient
configuration of system components, IoT enhances
the ability of MPPT algorithms to track and extract
the maximum power from the system [60].
Additionally, IoT facilitates the scalability and
integration of solar PV systems, making it easier to
manage both small and large installations. This
combination of MPPT and IoT is key to improving
the efficiency, reliability, and sustainability of solar
power generation.
Challenges and Limitations
While the integration of Internet of Things (IoT)
technology with solar photovoltaic (PV) systems
offers numerous advantages, it also presents several
challenges and limitations that need to be addressed
for optimal performance. These challenges span
technical, financial, and operational aspects, and they
must be carefully considered during the
implementation and scaling of IoT-enabled solar
systems.
1. Data Security and Privacy: One of the
primary concerns with IoT integration is
data security and privacy. Since IoT devices

57
are connected to the internet, they become
vulnerable to cyberattacks, which could
compromise the confidentiality and integrity
of the data they collect [61]. In the case of
solar PV systems, sensitive data such as
energy generation patterns, operational
status, and system performance is
transmitted and stored, making it an
attractive target for malicious actors.
Ensuring the security of this data is crucial,
requiring robust encryption methods, secure
communication protocols, and advanced
cybersecurity measures to safeguard against
potential breaches.
2. High Costs: Another significant limitation
of IoT integration is the associated costs.
The installation of IoT devices, such as
sensors, controllers, and communication
infrastructure, can be expensive.
Additionally, the need for cloud storage and
advanced data analysis capabilities to
process the massive amount of data
generated by IoT systems adds to the overall
cost of implementing IoT-enabled solar PV
systems [62]. For smaller-scale solar
installations, these costs may be
prohibitively high, making the widespread
adoption of IoT technology in such systems
more challenging. Overcoming this financial
barrier requires cost-effective solutions and
improvements in IoT hardware and software
technologies to make the integration more
affordable.
3. Connectivity and Reliability: The
effectiveness of IoT systems heavily relies on
stable and continuous internet connectivity
to transmit real-time data from the solar PV
system to centralized monitoring platforms.
However, in remote or rural areas, where
many solar PV systems are deployed,
internet connectivity may be unreliable or
even unavailable. In such regions, the lack of
stable communication channels can
significantly hinder the effectiveness of IoT
integration, as data may be delayed or lost
[63]. To address this challenge, it may be
necessary to explore alternative
communication technologies, such as
satellite communication or low-power wide-
area networks (LPWAN), to ensure reliable
data transmission in areas with poor internet
infrastructure.
4. Complexity in Data Management: IoT
systems generate vast amounts of data that
need to be managed, processed, and analyzed
in real time to optimize the performance of
solar PV systems. The sheer volume of data
can be overwhelming, making it difficult to
extract meaningful insights and make timely
decisions. Effective data management
strategies and algorithms are essential to
ensure that the data collected is not only
stored efficiently but also analyzed
accurately [64]. Additionally, the
complexity of managing such large datasets
can increase as the number of IoT devices
and solar systems grows, requiring advanced
machine learning and artificial intelligence
techniques to handle the data overload.
Finally, while the integration of IoT with solar PV
systems offers substantial benefits, several challenges
and limitations must be addressed to fully realize its
potential. Data security, high costs, connectivity
issues, and data management complexities are
significant hurdles that need to be overcome [64].
However, with continued advancements in
technology and the development of cost-effective
solutions, these challenges can be mitigated, allowing
for the broader adoption of IoT-enabled solar PV
systems and the optimization of solar power
generation.
FINDINGS
1. Enhanced Efficiency: The integration of
IoT with MPPT techniques allows for
continuous monitoring of environmental
conditions, which enables real-time
adjustments of operating points to ensure
maximum power extraction. This synergy
improves overall system efficiency by
adapting to changing factors such as
sunlight and temperature.
2. Real-Time Adaptability: IoT technologies
provide real-time feedback, allowing MPPT
algorithms to adjust dynamically to
fluctuating conditions. This enhances the
system's ability to maintain optimal
performance, even during transient weather
changes, such as cloud cover or shifting
irradiance.
3. Optimized System Configuration: IoT
allows for the monitoring of key system
components, such as inverters, batteries, and
solar panels. By analyzing data from these
components, IoT systems can facilitate
adjustments that enhance the system’s
operational efficiency, such as preventing

58
battery overcharging and optimizing energy
storage.
4. Scalability and Integration: IoT enhances
the scalability of solar PV systems by
enabling centralized monitoring of large-
scale solar farms or distributed systems.
This centralized management improves
decision-making and reduces the need for
manual intervention, ensuring seamless
operation and maintenance.
5. Challenges and Limitations: Several
challenges hinder the full implementation of
IoT in solar PV systems, including data
security concerns, high installation costs,
connectivity issues in remote areas, and the
complexity of managing large volumes of
data. These obstacles need to be addressed to
fully harness the benefits of IoT integration.
CONCLUSION
The integration of IoT with MPPT techniques
presents a transformative solution for optimizing the
performance of solar PV systems. IoT enhances
system efficiency, real-time adaptability, and
predictive maintenance, offering substantial
improvements in both residential and large-scale
installations. While challenges related to data
security, cost, and connectivity persist, ongoing
technological advancements in AI, machine learning,
and IoT are expected to address these issues, driving
further improvements in solar energy systems. The
future of solar PV systems, empowered by MPPT and
IoT, holds significant promise for achieving higher
efficiency, scalability, and sustainability in renewable
energy generation
REFERENCES
1. Conceptar M, Umaru K, Eze VHU, Jim M,
Asikuru S, Musa N, et al. Modeling and
Implementation of a Hybrid Solar-Wind
Renewable Energy System for Constant
Power Supply. Journal of Engineering,
Technology & Applied Science.
2024;6(2):72–82.
1. Ugwu CN, Ogenyi FC, Eze VHU.
Optimization of Renewable Energy
Integration in Smart Grids: Mathematical
Modeling and Engineering Applications.
RESEARCH INVENTION JOURNAL OF
ENGINEERING AND PHYSICAL
SCIENCES. 2024;3(1):1–8.
2. Eze VHU, Edozie E, Okafor OW, Uche
KCA. A Comparative Analysis of Renewable
Energy Policies and its Impact on Economic
Growth : A Review. International Journal of
Education, Science, Technology and
Engineering. 2023;6(2):41–6.
3. Eze VHU, Ukagwu KJ, Ugwu CN, Uche
CKA, Edozie E, Okafor WO, et al.
Renewable and Rechargeable Powered Air
Purifier and Humidifier : A Review. INOSR
Scientific Research. 2023;9(3):56–63.
4. Eze VHU, Uche KCA, Okafor WO, Edozie
E, Ugwu CN, Ogenyi FC. Renewable
Energy Powered Water System in Uganda :
A Critical Review. NEWPORT
INTERNATIONAL JOURNAL OF
SCIENTIFIC AND EXPERIMENTAL
SCIENCES (NIJSES). 2023;3(3):140–7.
5. Eze VHU, Edozie E, Umaru K, Okafor OW,
Ugwu CN, Ogenyi FC. Overview of
Renewable Energy Power Generation and
Conversion ( 2015-2023 ). EURASIAN
EXPERIMENT JOURNAL OF
ENGINEERING (EEJE). 2023;4(1):105–13.
6. Eze VHU, Eze MC, Chijindu V, Eze EC,
Ugwu AS, Ogbonna CC. Development of
Improved Maximum Power Point Tracking
Algorithm Based on Balancing Particle
Swarm Optimization for Renewable Energy
Generation. IDOSR Journal of Applied
Sciences. 2022;7(1):12–28.
7. Eze VHU, Innocent EE, Victor AI, Ukagwu
KJ, Ugwu CN, Ogenyi FC, et al. Challenges
and opportunities in optimizing hybrid
renewable energy systems for grid stability
and socio- economic development in rural
Sub-saharan Africa: A narrative review. KIU
Journal of Science, Engineering and
Technology. 2024;3(2):132–46.
8. Eze VHU, Eze CM, Ugwu SA, Enyi VS,
Okafor WO, Ogbonna CC, et al.
Development of maximum power point
tracking algorithm based on Improved
Optimized Adaptive Differential
Conductance Technique for renewable
energy generation. Heliyon [Internet].
2025;11(1):e41344. Available from:
https://doi.org/10.1016/j.heliyon.2024.e41
344
9. Stephen B, Abdulkarim A, Mustafa MM, Eze
VHU. Enhancing the resilience and
efficiency of microgrids through optimal
integration of renewable energy sources and
intelligent control systems : A review. KIU

59
Technology. 2024;3(2):21–38.
10. Eze VHU, Richard K, Ukagwu KJ, Okafor
W. Factors Influencing the Efficiency of
Solar Energy Systems. Journal of
Engineering, Technology & Applied
Science. 2024;6(3):119–31.
11. Eze VHU, Mwenyi JS, Ukagwu KJ, Eze MC,
Eze CE, Okafor WO. Design analysis of a
sustainable techno-economic hybrid
renewable energy system: Application of
solar and wind in Sigulu Island, Uganda.
Scientific African [Internet].
2024;26(2024):e02454. Available from:
https://doi.org/10.1016/j.sciaf.2024.e0245
4
12. Eze VHU, Edozie E, Umaru K, Ugwu CN,
Okafor WO, Ogenyi CF, et al. A Systematic
Review of Renewable Energy Trend.
NEWPORT INTERNATIONAL
JOURNAL OF ENGINEERING AND
PHYSICAL SCIENCES. 2023;3(2):93–9.
13. Eze VHU, Tamba II JS, Eze MC, Okafor
WO, Bawor FH. Integration of carbon
capture utilization and storage into
sustainable energy policies in Africa: the case
of Liberia. Oxford Open Energy.
2024;3(2024):oiae011.
Mathematical Optimization Techniques in
Sustainable Energy Systems Engineering.
SCIENCE. 2024;3(1):33–41.
15. Mwenyi SJ, Ukagwu KJ, Eze VHU.
Analyzing the Design and Implementation
of Sustainable Energy Systems in Island
Communities. International Journal of
Education, Science, Technology, and
Engineering (IJESTE). 2024;7(1):10–24.
16. Eze VHU, Bubu PE, Mbonu CI, Ogenyi FC,
Ugwu CN. AI-Driven Optimization of
Maximum Power Point Tracking (MPPT)
for Enhanced Efficiency in Solar
Photovoltaic Systems: A Comparative
Analysis of Conventional and Advanced
Techniques. INOSR Experimental Sciences.
2025;15(1):63–81.
17. Enyi VS, Eze VHU, Ugwu FC, Ogbonna
CC. Path Loss Model Predictions for
Different Gsm Networks in the University
of Nigeria , Nsukka Campus Environment
for Estimation of Propagation Loss.
International Journal of Advanced Research
in Computer and Communication
Engineering. 2021;10(8):108–15.
18. Eze VHU, Umaru K, Edozie E, Nafuna R,
Yudaya N. The Differences between Single
Diode Model and Double Diode Models of a
Solar Photovoltaic Cells : Systematic
Review. Journal of Engineering, Technology
& Applied Science. 2023;5(2):57–66.
19. Uche CKA, Eze VHU, Kisakye A, Francis K,
Okafor WO. Design of a Solar Powered
Water Supply System for Kagadi Model
Primary School in Uganda. Journal of
Science. 2023;5(2):67–78.
20. Eze MC, Eze VHU, Ugwuanyi GN,
Alnajideen M, Atia A, Olisa SC, et al.
Improving the efficiency and stability of in-
air fabricated perovskite solar cells using the
mixed antisolvent of methyl acetate and
chloroform. Organic Electronics [Internet].
2022 Aug;107:1–10. Available from:
https://linkinghub.elsevier.com/retrieve/pi
i/S1566119922001240
21. Eze MC, Ugwuanyi G, Li M, Eze VHU,
Rodriguez GM, Evans A, et al. Optimum
silver contact sputtering parameters for
efficient perovskite solar cell fabrication.
Solar Energy Materials and Solar Cells.
2021;230(2020):111185. Available from:
https://doi.org/10.1016/j.solmat.2021.111
185
22. Fred O, Ukagwu KJ, Abdulkarim A, Eze
VHU. Reliability and maintainability
analysis of Solar Photovoltaic Systems in
rural regions: A narrative review of
challenges, strategies, and policy
implications for sustainable electrification.
KIU Journal of Science, Engineering and
Technology. 2024;3(2):103–22.
23. Ukagwu KJ, Kapalata P, Eze VHU.
Automated Power Source Selection System
for Uninterrupted Supply: Integration of
Main Power, Solar Energy and Generator
Power. Journal of Engineering, Technology
& Applied Science. 2024;6(1):11–21.
24. Eze VHU, Oparaku UO, Ugwu AS,
Ogbonna CC. A Comprehensive Review on
Recent Maximum Power Point Tracking of
a Solar Photovoltaic Systems using
Intelligent , Non-Intelligent and Hybrid
based Techniques. International Journal of
Innovative Science and Research
Technology. 2021;6(5):456–74
Engineering Research. 2016;7(10):642–5.

60
25. Eze VHU, Iloanusi ON, Eze MC, Osuagwu
CC. Maximum power point tracking
technique based on optimized adaptive
differential conductance. Cogent
Engineering [Internet]. 2017;4(1):1339336.
Available from:
https://doi.org/10.1080/23311916.2017.13
39336
26. Eze VHU. Development of Stable and
Optimized Bandgap Perovskite Materials for
Photovoltaic Applications. IDOSR Journal
of Computer and Applied Science.
2023;8(1):44–51.
27. Eze VHU, Enerst E, Turyahabwe F,
Kalyankolo U, Wantimba J. Design and
Implementation of an Industrial Heat
Detector and Cooling System Using
Raspberry Pi. IDOSR Journal of Scientific
Research. 2023;8(2):105–15.
28. Eze VHU, Ugwu CN, Ogenyi FC.
Applications of Brain-Computer Interfaces
(BCIS) in Neurorehabilitation. Research
Output Journal of Biological and Applied
Science. 2024;3(1):35–9.
29. Eze VHU. Advanced Cryptographic
Protocols Using Homomorphic Encryption.
SCIENCE. 2024;3(1):80–8.
Integration of Topological Data Analysis
(TDA) in Structural Health Monitoring
(SHM) for Civil Engineering. RESEARCH
INVENTION JOURNAL OF
SCIENCE. 2024;3(1):23–32.
31. di KES, Umaru K, Eze VHU, Asikuru S,
Musa N, Ochima N. Voltage Optimization
on Low Voltage Distribution Transformer
Zones Using Batteries in Uganda. Journal of
Science. 2024;6(1):22–30.
32. Conceptar M, Umaru K, Eze VHU, Wisdom
OO. Design and Implementation of a DC to
AC Power Electronics-Based Inverter that
Produces Pure Sine Wave Output for
Critical Engineering Applications.
International Journal of Recent Technology
and Applied Science. 2024;6(1):1–13.
33. Ukagwu KJ, Isaac EA, Eze VHU, Chikadibia
KUA, Ukagwu F. Innovative Design and
Implementation of Portable and
Rechargeable Air Purifier and Humidifier.
International Journal of Recent Technology
and Applied Science. 2024;6(1):14–24.
34. Eze VHU. Advancing Sustainable Energy
Solutions in Uganda : A Comprehensive
Exploration for Multi-Source Power
Control Design. IAA Journal of Applied
Sciences. 2024;11(1):73–86.
35. Eze VHU, Tamball JS, Robert O, Okafor
WO. Advanced Modeling Approaches for
Latent Heat Thermal Energy Storage
Systems. IAA Journal of Applied Sciences.
2024;11(1):49–56.
36. Eze VHU, Robert O, Sarah NI, Tamball JS,
Uzoma OF, Okafor WO. Transformative
Potential of Thermal Storage Applications
in Advancing Energy Efficiency and
Sustainability. IDOSR JOURNAL OF
APPLIED SCIENCES. 2024;9(1):51–64.
Blockchain Technology in Clinical Trials.
Research Output Journal of Biological and
Applied Science. 2024;3(1):40–5.
Blockchain-Enabled Supply Chain
Traceability in Food Safety. Research
Output Journal of Biological and Applied
Science. 2024;3(1):46–51
39. Enerst E, Eze VHU, Musiimenta I,
Wantimba J. Design and Implementation of
a Smart Surveillance Secuirty System.
IDOSR Journal of Science and Technology.
2023;9(1):98–106.
40. Enerst E, Eze VHU, Ibrahim MJ, Bwire I.
Automated Hybrid Smart Door Control
System. IAA Journal of Scientific Research.
2023;10(1):36–48.
41. Eze VHU, Ugwu CN, Ugwuanyi IC. A Study
of Cyber Security Threats, Challenges in
Different Fields and its Prospective
Solutions : A Review. INOSR Journal of
Scientific Research. 2023;9(1):13–24.
42. Eze MC, Eze VHU, Chidebelu NO, Ugwu
SA, Odo JI, Odi JI. NOVEL PASSIVE
NEGATIVE AND POSITIVE CLAMPER
CIRCUITS DESIGN FOR ELECTRONIC
SYSTEMS. International Journal of
Scientific & Engineering Research.
2017;8(5):856–67.
43. Eze VHU, Eze MC, Ogbonna CC, Ugwu
SA, Emeka K, Onyeke CA. Comprehensive
Review of Recent Electric Vehicle Charging
Stations. Global Journal of Scientific and
Research Publications. 2021;1(12):16–23

61
44. Eze VHU, Eze MC, Ogbonna CC, Valentine
S, Ugwu SA, Eze CE. Review of the
Implications of Uploading Unverified
Dataset in A Data Banking Site ( Case Study
of Kaggle ). IDOSR Journal of Applied
Science. 2022;7(1):29–40.
45. Eze VHU, Onyia MO, Odo JI, Ugwu SA.
DEVELOPMENT OF ADUINO BASED
SOFTWARE FOR WATER PUMPING
IRRIGATION SYSTEM. International
Journal of Scientific & Engineering
Research. 2017;8(8):1384–99.
46. Eze VHU, Eze MC, Chidiebere CS, Ibokette
BO, Ani M, Anike UP. Review of the Effects
of Standard Deviation on Time and
Frequency Response of Gaussian Filter.
International Journal of Scientific &
Engineering Research. 2016;7(9):747–51.
47. Ogenyi FC, Buhari MD, Sadiq BO, Edozie E,
Eze VHU. A comprehensive review of
adaptive modulation and coding techniques
for spectrum efficiency and interference
mitigation in satellite communication. KIU
Technology. 2024;3(2):39–50.
48. Eze VHU, Tamball JS, Uzoma OF, Sarah I,
Robert O, Okafor WO. Advancements in
Energy Efficiency Technologies for
Thermal Systems: A Comprehensive
Review. INOSR APPLIED SCIENCES.
2024;12(1):1–20.
49. Okafor WO, Edeagu SO, Chijindu VC,
Iloanusi ON, Eze VHU. A Comprehensive
Review on Smart Grid Ecosystem. IDOSR
Journal of Applied Science. 2023;8(1):25–63.
50. Eze VH, Olisa SC, Eze MC, Ibokette BO,
Ugwu SA, Eze HU, Olisa SC, Eze MC,
Ibokette BO, Ugwu SA. Effect of input
current and the receiver-transmitter
distance on the voltage detected by infrared
receiver. International Journal of Scientific &
Engineering Research. 2016;7(10):642-5.
51. Enerst E, Eze VHU, Okot J, Wantimba J,
Ugwu CN. DESIGN AND
IMPLEMENTATION OF FIRE
PREVENTION AND CONTROL
SYSTEM USING ATMEGA328P
MICROCONTROLLER. International
Journal of Innovative and Applied Research.
2023;11(06):25–34.
52. Eze VHU, Edozie E, Ugwu CN. CAUSES
AND PREVENTIVE MEASURES OF
FIRE OUTBREAK IN AFRICA: REVIEW.
International Journal of Innovative and
Applied Research. 2023;11(06):13–8.
53. Enerst E, Eze VHU, Wantimba J. Design
and Implementation of an Improved
Automatic DC Motor Speed Control
Systems Using Microcontroller. IDOSR
Journal of Science and Technology.
2023;9(1):107–19.
54. Eze VHU, Uzoma OF, Tamball JS, Sarah NI,
Robert O, Okafor OW. Assessing Energy
Policies , Legislation and Socio-Economic
Impacts in the Quest for Sustainable
Development. International Journal of
Education, Science, Technology and
Engineering. 2023;6(2):68–79.
55. Eze VHU, Wisdom OO, Odo JI, N UC,
Chukwudi OF, Edozie E. A Critical
Assessment of Data Loggers for Farm
Monitoring : Addressing Limitations and
Advancing Towards Enhanced Weather
Monitoring Systems. International Journal
of Education, Science, Technology and
Engineering. 2023;6(2):55–67.
56. Eze VHU, Eze MC, Enerst E, Eze CE.
Design and Development of Effective Multi-
Level Cache Memory Model. International
Journal of Recent Technology and Applied
Science. 2023;5(2):54–64.
57. Ogbonna CC, Eze VHU, Ikechuwu ES,
Okafor O, Anichebe OC, Oparaku OU. A
Comprehensive Review of Artificial Neural
Network Techniques Used for Smart Meter-
Embedded forecasting System. IDOSR
JOURNAL OF APPLIED SCIENCES.
2023;8(1):13–24.
58. Eze VHU, Edozie E, Davis M, Dickens T,
Okafor WO, Umaru K, et al. Mobile
Disinfectant Spraying Robot and its
Implementation Components for Virus
Outbrea : Case Study of COVID-19.
International Journal of Artificial
Intelligence. 2023;10(2):68–77.
59. Edozie E, Dickens T, Okafor O, Eze VHU.
Design and Validation of Advancing
Autonomous Firefighting Robot. KIU
Technology. 2024;3(1):56–62
60. Abomhara M, Køien GM. Cyber security and
the internet of things: vulnerabilities,
threats, intruders and attacks. Journal of
Cyber Security and Mobility. 2015 May
22:65-88.
61. Haroon A, Shah MA, Asim Y, Naeem W,
Kamran M, Javaid Q. Constraints in the IoT:

62
the world in 2020 and beyond. International
Journal of Advanced Computer Science and
Applications. 2016;7(11).
62. López-Vargas A, Fuentes M, Vivar M. IoT
application for real-time monitoring of solar
home systems based on Arduino™ with 3G
connectivity. IEEE Sensors Journal. 2018
Oct 17;19(2):679-91.
63. Ansari S, Ayob A, Lipu MS, Saad MH,
Hussain A. A review of monitoring
technologies for solar PV systems using data
processing modules and transmission
protocols: Progress, challenges and
prospects. Sustainability. 2021 Jul
21;13(15):8120.
64. Gharaibeh A, Salahuddin MA, Hussini SJ,
Khreishah A, Khalil I, Guizani M, Al-Fuqaha
A. Smart cities: A survey on data
management, security, and enabling
technologies. IEEE Communications
Surveys & Tutorials. 2017 Aug
7;19(4):2456-501.
CITE AS: Enuru Holland Simera (2025). Solar Photovoltaic Maximum Power Point Tracking (MPPT)
and the Integration of IoT for Enhanced Performance and Monitoring. INOSR Scientific Research
12(1):53-62. https://doi.org/10.59298/INOSRSR/2025/12.1.536200

Solar Photovoltaic Maximum Power Point Tracking (MPPT) and the Integration of IoT for Enhanced Performance and Monitoring (www.kiu.ac.ug)

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