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Artificial Intelligence (AI) in Renewable Energy Systems: A
Condensed Review of its Applications and Techniques
Jeffrey T. Dellosa
Department of Elecronics Engineering
Caraga State University
Butuan City, Philippines
jtdellosa@carsu.edu.ph
Eleonor C. Palconit
College of Engineering and Architecture
Ateneo de Davao University
Davao City, Philippines
ecpalconit@addu.edu.ph
Abstract— This paper's main objective is to examine the
state of the art of artificial intelligence (AI) techniques and tools
in power management, maintenance, and control of renewable
energy systems (RES) and specifically to the solar power
systems. The findings would allow researchers to innovate the
current state of technologies and possibly use the standard and
successful techniques in building AI-powered renewable energy
systems, specifically for solar energy. Various peer-reviewed
journal articles were examined to determine the condition and
advancement of the AI techniques in the field of RES,
specifically in solar power systems. Different theoretical and
experimental AI techniques often used and reliable techniques
determined were the Artificial Neural Network (ANN),
Backpropagation Neural Network (BPNN), Adaptive Neuro-
Fuzzy Inference System (ANFIS), and Genetic Algorithm (GA).
These techniques are widely used in different types of solar
predictions based on the findings of this review. However, ANN
stood out as the best of these techniques. ANN's specific
advantages over its competition include short computing time,
higher accuracy, and generalization capabilities over other
modeling techniques. This would translate to cost efficiency over
other modeling techniques.
Keywords— Artificial Intelligence (AI), Renewable Energy
Systems (RES), Solar PV systems
I. INTRODUCTION
The use of renewable energy systems (RES) to supplement
energy insufficiency was demonstrated, according to previous
studies [1-7]. Renewable energy, unlike fossil fuels, deliver
clean energy to households with a minimal carbon footprint.
More importantly, several studies have shown the use of RES
reduces greenhouse gases (GHGs) in the atmosphere [8-10].
Renewable energy systems (RES) include systems that
generate energy from environmental sources. Sources include
the sun (solar), water (hydroelectric), heat derived from the
grounds of the earth (geothermal), wind, plants (biomass),
waves from the ocean, and even the temperature difference of
the sea or the ocean (ocean thermal energy conversion or
otherwise known as OTEC).
Solar photovoltaic (PV) energy is a renewable energy
source that captures available sunlight and converts them to
electric power. Solar is widely used in both developed and
developing countries in the world, as reported by several
authors [11-13]. There are at least 402 Gigawatts of combined
solar capacity for all countries in the world as of the end of
2017 [14]. China has the highest number of solar power
deployments among all developed countries [15]. These
massive deployments in solar power in developed countries
are also being translated as a solution to the problems being
encountered by those countries with no access to electricity,
especially in rural areas.
A. Environmental, social, and technical impact of RES
Several studies have already been done on the RES’
environmental, social and technical feasibility, making RES a
viable option for use going forward to the future [16-23].
Akella, et al. thoroughly discussed the technical and
environmental impact of the deployment of renewable energy
systems. The advantages and benefits of using clean energy
systems compared to the traditional oil, natural gas, or coal-
based energy generation systems were presented. In this case
study, the renewable energy systems were installed to supply
clean energy to the unelectrified villages [24].
B. The Need for AI in RES
The use of RES has also evolved and advanced. Still, there
exist opportunities with regards to the use of smart and highly
intelligent systems, particularly the application of artificial
intelligence (AI) to address the present evolving challenges
and opportunities in renewable energy and solar photovoltaic
systems' power management, maintenance, and control [25-
29].
AI has several applications in broad scope or area of
concern that includes not only the energy sector but in food,
agriculture, education, health & safety, and even in business
and the art, among others [30]. Before AI, there exist basic
decision systems in renewable energy that includes data
acquisition and monitoring systems [31-34].
The review's main objective is to examine state of the art
using artificial intelligence (AI) techniques and tools in power
management, maintenance, and control of renewable energy
and specifically to the solar power systems. The last review
related to AIs [35]. This study's findings shall allow
researchers to innovate the current state of technologies and
possibly use the standard and successful techniques in
building AI-powered renewable energy systems.
II. METHODOLOGY
A literature review from highly regarded journals and
reviews of the reports related to the study in the last ten years
was conducted to draw meaningful and significant results
from artificial intelligence (AI) studies, specifically in the
field of power management, maintenance, and control of solar
energy systems. This study covered the current trends in AI as
applied in renewable energy and specifically to solar energy
systems. This study considered the different techniques in
using artificial intelligence to improve solar energy systems'
performance and efficiency. Explored in this literature review,
are those AI techniques and descriptive analysis of renewable
energy techniques, such as solar energy.
III. RESULTS AND DISCUSSIONS
A. AI Applications in General
AI is a computer science and engineering field that
focuses on creating smart or intelligent machines, devices,

2. and systems that perform operations similar to human
learning and decision-making [36]. It can also be defined as
a system's ability to understand external information
properly, learn from that information, and practice those
learnings to accomplish definite goals and tasks over flexible
adaptation [37].
AI can be simply described as providing intelligence to
machines that can process information and makes decisions,
just the way human beings think of all information received
and act on that information when needed. It is used to make
machines or entities intelligent to perform tasks efficiently for
complex problems [38-39]. Still, it has yet to achieve the level
of intelligence of the human brain fully. With this kind of
description, robots always come to mind. Machines can only
act similar to human beings, if and only if, devices have rich
data relating to their environment. Machines needed to have
proper and a large chunk of information to make sense of
what is happening around their environment to solve complex
problems.
For AI to simply work and provide value, it needs IoTs
comprising of sensors and other data-gathering tools to
collect the required information from the environment it
needed. Those collected data are stored with proper
configurations and are analyzed. With systems capable of
collecting tens or hundreds of millions or massive volumes of
data gathered from the environment and stored in the
database, the process of storing such information and the
analysis becomes more complicated. The data collected are
commonly analog formats often gathered from the
environment and included texts or numbers. Environmental
information that provides for temperature and humidity can
be another form of data.
Artificial intelligence introduces techniques and
methodologies for a machine, device, or a robot to make
sense of the information collected and processed. Machines
are configured to self-learn with the information available
and act on the corresponding inputs from the environment.
This artificial intelligence in the apparatus, devices, or
systems leverages on the IoTs, big data, and AI.
For a device, system, or a machine to fully imitate all
aspects of the human being, they need to have all four
fundamental characteristics.
AIs are also classified as analytical, human-inspired, or
humanized AIs [37]. The following are their definitions:
• Analytical AIs - producing a cognitive representation of
the environment and learn based on the historical
understandings or events to apprise forthcoming
choices;
• human-inspired AIs – comprehend human feelings and
study them in making choices for humanized AI;
• humanized AIs - these systems are designed to be self-
conscious in their exchanges with others.
One specific AI application in university is the AI-based
virtual teaching assistant (VTA) implemented in Georgia
Tech to answer students' queries in their virtual library. Jill
Watson is the name of the virtual assistant of the university
[37].
B. AI Applications and Techniques
AI improves how people work today, especially on the
improvement or shift from manual computations. In several
studies, AI has proven its use in the field of medicine or
healthcare and even in accounting databases and computer
games [40-45].
AI is extensively used in agriculture, specifically on the
applications in rice diseases, crop management, pest
management, product monitoring and control, soil and
irrigation management, weed management, and yield
prediction, among others [46-48]. In medicine and healthcare,
diagnostic imaging is considered as top area where AI is
extensively used in research, followed by genetic and
electrodiagnosis.
In medicine, AI has done wonders to understand better
diseases that include the use of AI tools in cancer, disorders in
the brain (stroke), and the heart. AI tools are often used to
detect and diagnose early warning signs of diseases, treatment,
and prediction of an outcome and prognosis evaluation [49-
50]. Several studies have also shown that AI can be used in
biometric and forensics [51-53]. There seem to be endless
possibilities of the use and applications of AI.
1. AI Tools and Techniques
AI was founded on different learning theories. That
includes evolutionary learning, statistical learning, and neural
learning [54-55]. Neural Network (NN) and Support Vector
Machines (SVM) are the two most common learning
algorithms used in many works of literature [56]. Other
algorithms include Linear Regression, Nearest Neighbor,
Naïve Bayes, Logistic Regressions, Random Forests,
Decision Tree, and Hidden Markov [56].
2. Neural Network
Several studies were conducted in Neural Network,
specifically in the Artificial Neural Network or ANN [57-61].
The ANN was founded in 1943 by McCulloch and Pitts. ANN
is considered the most ultimate AI technique about the
mathematical model for the human brain's (neuron) primitive
cell [62-63]. The mind is activated when the input's weighted
total surpasses the desired value wherein the output responds
to the activated functions.
The ANN can fine-tune the figures to correct the output's
fault, making that a very controlling tool for machine learning
[56]. Fig. 5 shows a simple illustration of a neural network and
the relationship between the input and output and the outcome.
3. Support Vector Machines
The SVMs are typically used to classify objects in two
groups, which are managed to learn models with associated
learning procedures. The data are analyzed and used for
classification and regression analysis. SVMs can also
proficiently accomplish a non-linear classification using a
technique or process called kernel trick wherein indirectly
charting their inputs into feature spaces with high dimensions
[63-64].
The classification tasks are not considered simple and are
always regarded as complicated, and with that, complex
structures are required for the optimum object separation [65].

3. C. AI in Renewable Energy Systems
Renewable energy systems are becoming advanced by
introducing smarter systems such as IoT and AI being
integrated to make these RES robust and more responsive. AI
technologies are now being used in all RE systems from wind,
solar, hydro, ocean, geothermal to solar photovoltaic systems,
among others [62]. It has shown that with the use of AI in RE
systems, it made the technologies like the panels, solar PV,
and wind turbines systems becoming more cost-effective and
efficient, thus become more ubiquitous and with the potential
outcome of removing the fossil fuel-based power plants [66].
The following are the different AI techniques being used
in renewable energy systems: support vector machines
(SVM), Feed-forward backpropagation neural network
(BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS),
Naïve Bayes (NB), Genetic Algorithm (GA), Artificial Neural
Network (ANN), Radial basis function neural networks
(RBFNN), and seasonal autoregressive integrated moving
average (SARIMA), HIstorical SImilar Mining (HISIMI),
Support Vector Machines (SVM), Transient System
Simulation Tool (TRNSYS), Group method of data handling
neural network (GMDHNN), Radial basis function (RBF),
autoregressive integrated moving average (ARIMA). ANN
stood out as the best of these techniques [67-71].
Among these techniques came a more versatile one, the
ANN. ANN's specific advantages over its competition include
short computing time, higher accuracy, and generalization
capabilities over other modeling techniques. This would
translate to cost efficiency over other modeling techniques
[72]. The same study covers many applications of ANN
techniques in various fields that include medicine, science,
engineering, environmental, agriculture, mining, technology,
climate, business, arts, and nanotechnology, among others.
The study assesses ANN contributions, compare
performances, and critiques methods. Neural-network models
such as feedforward and feedback propagation artificial neural
networks perform better in applying to human problems [72].
The same study determined that the feedforward and feedback
propagation ANN models are the best fit for data analysis
factors like accuracy, processing speed, latency, fault
tolerance, volume, scalability, convergence, and performance.
1. AI Applications in Solar Energy
The significance of the AI applications in solar power
systems is detailed in different review articles [74-76].
The AI-based studies of Mellita et al. focus on the design
and sizing of the solar photovoltaic (PV), which is considered
one of the most important considerations. As mentioned, AI
methodologies are used in many applications. One such
widespread use is solar radiation modeling, prediction, and
forecasting. Among the many techniques introduced in solar
energy literature, the Artificial Neural Network (ANN) was
the most often used method according to the studies published
[77-102].
It has been determined from these studies that there are
correlations that exist with 98-99% and 94-96% between the
actual and the predicted solar irradiance during the sunny days
and cloudy days, respectively [103].
TABLE I. THE SUMMARY OF AI TECHNIQUES OR METHODS USED IN
DIFFERENT APPLICATIONS FROM AMONG THE 40 SOLAR ENERGY RESEARCH
PUBLICATIONS
Methods Used Applications
Backpropagation
neural network
(BPNN)
Solar irradiance prediction, solar radiation
prediction, water heating system, beam
radiation prediction, daily ambient
temperature prediction, daily solar
irradiation prediction, maximum power of
HCPV prediction, global solar irradiation
prediction,
Solar energy and hot water quantity
prediction, Solar energy prediction, and
building energy prediction.
ANFIS PV power supply modeling, Hourly global
irradiance prediction, Clearness index,
radiation prediction, PV power supply
modeling, Solar power prediction, and SCPP
performance prediction
GA Solar tracking, Design of solar water heating system
BPNN and Batch
Learning ANN
Prediction of mean temperature
BNN and Angstrom
linear methods
Prediction of global solar radiation
BNN and Regression
Methods
Prediction of global solar radiation
GA+HISIMI Prediction of solar power
RBF+IIR and
BPNN+IIR
Optimization of PV systems
ANN+TRNSYS Prediction of performance of ICS
WT+BPNN Values estimation of solar radiation
PPF, Support Vector
Machines Cloudy
Prediction of solar power
BPNN + GA Prediction of solar power
GA, PO MPPT of PV array
GA+GMDHNN Optimization of solar power systems
RBFNN+WT Prediction of PV energy
SVR, BPNN PV energy prediction
Support Vector
Machines, RBFNN,
Autoregressive
Prediction of solar power
Many researchers have been using BPNN to predict solar
irradiance, solar radiation, solar energy, water heating system,
beam radiation, daily ambient temperature, daily solar
irradiation, and a maximum power of HCPV prediction.
Researchers also used BPNN for global solar irradiation
prediction, solar energy and hot water quantity prediction,
Solar energy prediction, and building energy prediction.
ANFIS was used in PV power supply modeling, Hourly global
irradiance prediction, clearness index, radiation prediction,
PV power supply modeling, solar power prediction, and SCPP
performance prediction. There were instances wherein a
combination of different techniques is applied to obtain
optimum results in predictions, estimations, and forecasting.
The root-mean-square error (RMSE) is often used to
determine the differences between the sample or population
values predicted by a given model and the observed amounts.
It is used as a typical statistical metric to quantify model
performance, such as in meteorology or air quality [104].
2. AI Applications in Solar Microgrid
AI applications are considered significant as well in
the solar microgrid applications. A summary of the findings
can be found in Table II.

4. TABLE II. THE SUMMARY OF AI TECHNIQUES OR METHODS USED IN A
SOLAR MICROGRID.
Author &
Year of
Publica-tion
AI
Techniques
Involved
Application Findings
[104] Bacterial
foraging
optimization
(BFO);
Particle
swarm
optimization
(PSO);
GA;
Tuning of the
significant
parameters’
in automatic
generation
control in
microgrid.
BFO is superior
to PSO and GA
in the
simulations
conducted with
different
parameters in
the microgrid.
[105] ARIMA time
series
algorithm
using
continuous
data.
Electric
consumption
prediction in
a solar PV
microgrid
village.
There was a
correlation on the
predicted and
actual values.
[106] Machine
learning
algorithm
using
regression
tree model
Solar
microgrid
power
generation
output
prediction
Predicted versus
actual results
obtained 85%
and 77%
accuracy (AM
and PM
respectively.
[107] Artificial
neural
network
(ANN) was
used for the
prediction
model.
Solar energy
output
prediction
Predicted versus
actual output
was at 0.5–9%
difference.
[108] Sugeno type-
fuzzy system;
Q-learning
algorithm
Energy
management
Indicated a good
performance of
the Sugeno type-
fuzzy system.
IV. CONCLUSION
In this paper, the AI applications were presented in
different industry segments, such as medicine, agriculture,
education, corporations, and the government, specifically in
the introductory part. AI applications and techniques in RES
were discussed coming from various journal articles.
Different authors used several AI methodologies. In solar
energy, the most popular methods used were ANN BPNN,
ANFIS, and GA. These techniques are widely used in
different types of solar predictions based on the findings of
this review. ANN stood out as the best of these techniques.
ANN's specific advantages over its competition include short
computing time, higher accuracy, and generalization
capabilities over other modeling techniques. This would
translate to cost efficiency over other modeling techniques.
Future work in AI is a combination of other approaches to
further optimize prediction models' accuracy, especially in
solar radiation predictions.
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