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Contents list available at CBIORE journal website
International Journal of Renewable Energy Development
Journal homepage: https://ijred.cbiore.id
Harnessing artificial intelligence for data-driven energy predictive
analytics: A systematic survey towards enhancing sustainability
Thanh Tuan Le1, Jayabal Chandra Priya2,
*
, Huu Cuong Le3, Nguyen Viet Linh Le4,*,
Minh Thai Duong5,*, Dao Nam Cao6
1Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam.
2Department of Computer Science & Engineering, Mepco Schlenk Engineering College, Sivakasi, Virudhunagar, Tamil Nadu, India
3Institute of Maritime, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam.
4Faculty of Automotive Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam.
5Institute of Mechanical Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam.
6PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam.
Abstract. The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion
and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient
energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control
applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges
is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that
predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across
various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from
2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance
and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy
management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The
study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting,
power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred
that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions
in this field.
Keywords: Artificial intelligence; Machine learning; Energy forecasting; Artificial Neural Network; Energy management, Predictive Analytics, Energy
sustainability
@ The author(s). Published by CBIORE. This is an open access article under the CC BY-SA license
(http://creativecommons.org/licenses/by-sa/4.0/).
Received: 26th Dec 2023; Revised: 16th January 2024; Accepted: 10th Feb 2024; Available online: 21st Feb 2024
1. Introduction
Nowadays, the countries, scientists, and policymakers are
paying much attention to energy sectors and the use of clean
energy such as renewable energy, hydrogen, and bioenergy
aiming to achieve the critical goals of decarbonization and
climate change, as well as diversification of energy sources
(Hoang et al., 2023a, 2023b; X. P. Nguyen et al., 2021; Pollet and
Lamb, 2020). However, an emerging issue in using such energy
sources is the management one. Throughout the evolution of
energy management, significant attention has been directed
toward investigating the application of predictive analytics
(Tarasiuk et al., 2023). This recognition stems from its pivotal
role in enhancing energy efficiency, integrating renewable
energy sources, ensuring grid stability, enabling demand
response programs, informing energy planning and policy
formulation, and reducing costs for consumers (Nguyen et al.,
2024; Seutche et al., 2021). Researchers leverage advanced data
analytics techniques, such as pattern analysis and forecasting
*
Corresponding authors
Email: jchandrapriya@mepcoeng.ac.in (J.C.Priya); linh.lnv@vlu.edu.vn (Ng.V.L.Le); thai_ck@ut.edu.vn (M.T.Duong)
models, intending to optimize energy utilization, minimize
waste, and accurately predict energy demand (Alsafasfeh, 2020;
Anandika et al., 2023; Sarwosri et al., 2023). This empowers
businesses, industries, and households to make data-informed
decisions, implement energy conservation measures, and
efficiently manage energy resources (Ramirez-Sanchez et al.,
2022). The integration of intermittent renewable energy sources
poses challenges, and predictive analytics plays a crucial part in
forecasting renewable energy generation to facilitate its
seamless integration into the grid (Adhikari et al., 2024; Ugwu et
al., 2022). Additionally, precise prediction of energy demand
enables proactive measures for load balancing, demand
response, and grid stability (Chandrasekaran et al., 2019; Wang
et al., 2016). By providing valuable insights into energy patterns,
researchers assist policymakers in formulating sustainable
energy strategies, establishing targets for renewable energy
adoption, and making informed decisions regarding
infrastructure investments and energy transformation progress
Review Article
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(Hoang et al., 2021; Ilham et al., 2022). Ultimately, the objective
of research in energy management with predictive analytics is
to establish a more resilient, dependable, and cost-effective
energy system to ensure a sustainable future. The global issue
of energy scarcity is increasingly severe due to the emergence
of the world's oil crisis and resource shortages (Kian and Lim,
2023). In the next three decades, it is expected that the
consumption of renewable energy in the world increase by
147% (Statista, 2019). Interestingly, new worldwide investments
in green energy were just about ten times higher in 2019 than
those in 2004. Moreover, renewable energy has grown its share
of worldwide energy production to 13.4% in 2019 from 5.2% in
2007 (Statista, 2013). Speaking of all green energy sources,
electricity's role has grown by a ratio of two to three than ever
before, implying that every resource of the electrical system
should be effectively exploited to benefit society (Lopes et al.,
2007; Nguyen et al., 2022). Energy demand that varies
stochastically could create a mismatch between the demand
and supply of energy, which leads to the instability of the
system’s operation (Ullah and Baseer, 2022; Wattana and
Aungyut, 2022). More interestingly, incentivization is known as
a type of energy management method in which prosumers
(known as the consumers that produce and use small-scale
energy, so-called energy districts) are encouraged to plan their
loads at specific time periods (demand-side management)
(Lagouir et al., 2021; Lahlou et al., 2023). Accordingly, smart
energy management is required to track and coordinate the
capacities and requirements of all consumers, resources and
suppliers, energy market players, infrastructure operators, as
well as energy transformers (Li et al., 2023; Nižetić et al., 2023;
Rowlands et al., 2011). Scientists have been studying ways to
create a complete energy management model that helps not
only the grid but also prosumers over the past several decades.
Indeed, methods and optimization algorithms for managing
energy are gradually integrated into the energy management
model to provide dependable, clean, and cheap energy
(Ağbulut, 2022a; Jawad et al., 2021; Li and Jayaweera, 2015). In
power networks, optimization methods are used to manage the
demand and supply of energy in order to meet economic load
delivery, quality of service, and system reliability (Bakay and
Ağbulut, 2021; Jawad et al., 2021). More significantly, an
effective optimization method requires well-defined criteria,
specifications, as well as system prerequisites. If there are any
changes in the system specification, like changeable energy
supply because of renewable sources of energy or modified
requirements of prosumers, the optimization issue must be
reformulated to accommodate the new parameters. In fact,
important studies in the field of energy management related to
prosumers and applications of smart grids have been carried out
(Jadhav and Patne, 2017; Jawad et al., 2021; Li and Jayaweera,
2015; Park et al., 2016). However, significant progress is
required in energy-efficient algorithms, energy management
models, energy estimation, transmission, and management
(Ahmed et al., 2020a; Jadhav and Patne, 2017; Kucęba et al.,
2018; Park et al., 2016).
Artificial intelligence (AI) including machine learning (ML)
and combined algorithms can be utilized in many fields such as
energy and fuels (Drzewiecki and Guziński, 2023; Goyal et al.,
2023; Sharma et al., 2023), education (Haque et al., 2024; Kim,
2024; Kim et al., 2023), communication (Hu and Qin, 2017;
Melinda et al., 2024; Rumapea et al., 2024), chemical
engineering (Aniza et al., 2023; Dobbelaere et al., 2021),
industry manufacturing (Chau et al., 2021; Lee et al., 2018),
transportation and logistics (Hu, 2018; H. P. Nguyen et al., 2023;
Radonjić et al., 2020; Witkowska and Rynkiewicz, 2018; Zaki,
2024), medical (Haleem et al., 2019; Pang et al., 2023; Yunidar
and Melinda, 2023), social study (Liu et al., 2023; Triandi et al.,
2023), environment (Biswas et al., 2023; Chaoraingern et al.,
2023; Domachowski, 2021; Vo et al., 2021), and economy
(Furman and Seamans, 2019; Suvon et al., 2023) aiming to
enhance management effectiveness. For energy area, AI could
be used for forecasting energy production and demand
prediction (Aguilar et al., 2021; Ahmad et al., 2021; Mosavi et al.,
2019), energy theft detection (Ahmad et al., 2021), demand side
management (Antonopoulos et al., 2020), predictive
maintenance and monitoring (H. P. Nguyen et al., 2021;
Wedashwara et al., 2023), optimized energy operation (El-
Shafay et al., 2023; Goswami et al., 2022), energy pricing and
energy-related emission prediction (Ağbulut, 2022b; Mosavi et
al., 2019), weather phenomena prediction associated with
forecast (Ihsan et al., 2023; Mosavi et al., 2019), energy
management and waste-to-energy management (Abdallah et al.,
2020; Sharma et al., 2022a). It is noted that solar energy, wind
power, hybrid energy, geothermal energy, hydrogen energy,
bioenergy, biofuels, biomass, and ocean energy can all employ
AI models (Ağbulut et al., 2021; Chen et al., 2021; W.-H. Chen et
al., 2022b, 2022a; Jha et al., 2017; Tabanjat et al., 2018; Tuan
Hoang et al., 2021). Besides, Support Vector Machines (SVMs),
Artificial Neural Networks (ANNs), Ensemble, Wavelet Neural
Networks (WNNs), SHapley Additive exPlanations, and
Decision Trees are some examples of AI algorithms (de Ville,
2013; Le et al., 2023; Li et al., 2023; V. G. Nguyen et al., 2023;
Said et al., 2022; Sharma et al., 2022b; Veza et al., 2022a; Zhang
et al., 2022). Moreover, in the smart grid setting, the algorithms
are extensively employed for a variety of issues including
energy reliability, prediction, and management. For instance,
the day-ahead consumption of energy of air conditioners in the
intelligent grid was forecasted in the research mentioned in
(Chou et al., 2019a) aiming to evaluate the algorithms' efficacy.
Moreover, the effectiveness of a hybrid SVM as well as ANN for
protective network architecture and settings was investigated to
guarantee the dependability of microgrids (Ahmed et al., 2020a;
Lin et al., 2019). AI techniques hold the potential to be deployed
across a broad spectrum of energy control tasks. The motivation
behind this work is to segregate the findings documented in this
field, contextualized within the framework of autonomic
computing, with the ultimate goal of achieving optimal energy
control. The primary objective of this article is to critically
evaluate and assess the appropriateness of utilizing AI
techniques in energy management, incorporating contemporary
concepts like autonomous computing to effectively organize
raw data. The sub-objectives include:
• Examining the current state of AI adoption in the energy
sector,
• Identifying the challenges and opportunities of using AI
for energy predictive analytics,
• Discussing the potential benefits of AI for sustainability,
• Proposing a roadmap for the future adoption of AI in the
energy sector.
Consequently, this research paper significantly advances
our comprehension of feasible AI-driven energy management
techniques. In this paper, we make the following key
contributions to the field of AI for energy management:
✓ The paper discusses various applications of AI in
energy management, including energy forecasting for demand
and supply, demand response to manage energy demand, and
the use of AI in managing smart grids to improve reliability,
security, and efficiency.
✓ The paper discusses the use of intelligent algorithms that
mimic human communication, decision-making, and
formal logical reasoning. It emphasizes the use of
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mathematical rules based on degrees of membership
and highlights the adaptability and learning capabilities
of these algorithms.
✓ The paper explores the use of ANNs in energy
management systems. It provides an overview of the
structure and functioning of ANNs, including neuron
representation and activation functions. It emphasizes
the importance of selecting appropriate input and output
variables for ANNs.
✓ The paper reviews the development of a forecast engine
for estimating energy with heuristic and metaheuristic
optimization paired with ANN. Additionally, the paper
explores the influence of issue dimensions on the
accuracy of the ANN model for solar power systems.
2. Research methodology
The primary objective of this literature review is to analyze
the current state of the art in energy prediction and
management and offer an extensive review of the extant
literature. Computational prognostication plays a pivotal role in
proficiently strategizing and optimizing the scheduling of the
energy system. This study entails conducting a comparative
analysis of diverse machine learning techniques applicable to
the forecasting of time series data.
Research Query 1: What are the current and emerging
applications of artificial intelligence (AI) in energy management,
and how can AI algorithms be utilized to optimize energy
production, consumption, and distribution to address the
challenges of energy scarcity and the transition toward
renewable energy sources?
Research Query 2: How can artificial neural networks (ANNs)
be effectively utilized to optimize fuel economy and energy
efficiency in vehicles, forecast solar power production, predict
electricity demand, and optimize energy storage systems, while
also improving the performance and accuracy of energy
forecasting models, particularly SVM and its variants, for
various applications in the energy management domain?
Research Query 3: How can hybrid energy systems benefit
from the optimized implementation of predictive control
methods utilizing neural networks and fuzzy logic-based energy
management systems, to achieve greater energy efficiency, user
comfort, and effective power flow governance while adapting to
changes in system configuration?
To ensure comprehensive coverage of the relevant
literature, the Scopus and Web of Science databases were
searched. The outcomes of every query were organized based
on their relevance and the initial 600 were subjected to manual
scrutiny. Additionally, conference papers were disregarded
search to concentrate on the most superior works. The search
scope was confined to papers published from 2003 onwards.
The literature exploration encompassed articles released
between 2003 and 2024. This period was chosen to encompass
the latest advancements in research while also acknowledging
foundational studies that offer a historical framework for the
field. The search strategy utilized a combination of relevant
keywords and Boolean operators to retrieve articles that closely
align with the research queries. The keywords were selected
based on their relevance to the research area and their ability to
capture the key concepts and themes within the field. Each of
the queries was performed separately in each of the databases
in the following Table 1.
These articles underwent a systematic screening process to
determine their eligibility for inclusion in the review paper. The
paper selection process involved the application of predefined
inclusion criteria. To be incorporated into the taxonomy, an
article needed to present a machine learning (ML)-based
solution that could be effectively employed for energy
prediction. The selection criteria were as follows:
• The ML solution proposed must have direct applicability
for the implementation of energy predictive analytics.
• Articles that merely discussed machine learning
techniques in the literature review or future work
section, without actually implementing a machine
learning solution, were excluded from consideration.
• Review papers were intentionally omitted, although
pertinent review papers are referenced in Section 1.
• Articles focusing on machine learning applications
related to low-level energy management were not
included in the selection process.
• The taxonomy specifically concentrates on machine
learning applications for energy optimization and
modeling, deliberately excluding energy policies and
regulations.
• The taxonomy solely concentrates on the domains of
analysis and planning, excluding considerations of
demand-side management and supply-side
management.
3. Artificial intelligence
Artificial Intelligence is characterized as the cognitive ability
of an artificial agent to effectively traverse complex problem
domains associated with a system conventionally attributed to
a machine or a computational device (Bisri and Man, 2023;
Luger, 2005). AI is an interdisciplinary field that integrates the
paradigms of physiology and computer science wherein
intelligence is conceptualized as the computational aspect of the
capacity to effectively achieve objectives on a global scale
(Kumar and Thakur, 2012), as shown in Fig. 1.
Intelligent algorithms encompass a logical construct that
comprehends values beyond the binary concepts of true and
false (Hasnaoui et al., 2023). The purpose of augmenting
intelligence is to emulate human capabilities for
communication, rational decision-making, and application of
common sense (Duraković and Halilovic, 2023). Zadeh
(Zadeh, 1965) defined intelligent algorithms as a collection of
mathematical rules for representing knowledge determined
Table 1
Search Query Formulation
Query
Search keyword
Scopus
ScienceDirect
IEEE Xplore
Q1
“Energy” AND “Learning”
4890
2990
489
Q2
“Residential Energy” AND “Learning”
887
686
103
Q3
“Smart meter” AND “Artificial Neural Network”
591
340
87
Q4
“Transport” AND “Artificial Neural Network”
520
119
96
Q5
“Construction” AND “Artificial Neural Network”
479
98
79
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based on the degrees of membership rather than the crisp
membership in traditional binary logic (Natsheh, 2013).
Intelligent algorithms can also be characterized as the
computational process that autonomously generates the
optimal results in response to varying inputs. Additionally,
multiple smart programs collaborating can provide the AI with
its adaptive capabilities. More remarkably, unlike a fixed
mathematical formula, several of these algorithms depend on
training and they could be updated to enhance their
performance, whereas others can modify their actions
depending on outputs and inputs, hence making them more
broadly useful. This study presents a comprehensive depiction
of the predominant intelligent models or algorithms that have
been extensively utilized. The findings are derived from an
extensive analysis of the academic literature, encompassing 581
scholarly articles published within the period spanning from
2017 to 2022. The research proceeded by selecting the most
representative and recent algorithms in the state-of-the-art
literature. Fuzzy logic (FL) and neural networks (NN) appear to
be the most common methods. As shown in Fig. 2, a taxonomy
of artificial intelligence (AI) for energy management is depicted.
3.1. Artificial neural network for Energy Management and
Forecasting
It is noticeable that a multiprocessor processing system is a type
of artificial neural network (ANN), and this system is made up
of a series of very basic and highly linked processors known as
neurons, which are similar to biological neurons in the human
brain (Rangkuti et al., 2023; Razak Kaladgi et al., 2021). The
flowchart illustrating the process of ANN development for
testing and training is depicted in Fig. 3.
In addition, Fig. 4a illustrates the fundamental model of a
solitary neuron. The bias b has an impact on the activation
function f by shifting it to the left or right, based on whether it is
negative or positive. More interestingly, a collection of
activation functions can be used to select the activation function
f (as a sigmoid function, hard limit function, and piecewise-linear
Fig. 1. Generalized learning process of artificial intelligence
Fig. 2. Survey taxonomy
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function). Also, Fig. 4b depicted some of the most common
activation functions (Natsheh, 2013). Researchers have also
investigated ANNs aiming to build energy management
systems. In general, choosing output and input variables for an
ANN has a large impact on the performance and the utilization
or generalization of the network (Al Sasongko et al., 2022;
Rudzki et al., 2022; Zheng Chen et al., 2014). It is noticed that
the ECMS, an instantaneous optimization algorithms'
representative, is considered the most potential online EMS and
is currently widely applied in the real world (Kommuri et al.,
2020; Wang and Huang, 2020). The process of converting fuel
to electricity is carried out by adding an equivalent factor (or EF
for short) which calculates the electrical energy cost as a fuel
consumption's equivalent quantity. Hence, to achieve optimum
energy savings, it is suggested that the EF should be a variable
number that is dynamically tuned based on real-time powertrain
activities. As a result, several EF estimates approaches have
been developed to adaptively control EF while taking into
account the vehicle state and driving circumstances. In the case
of HEV applications, it is assumed that the equivalent factor is
adjusted based on factors relating to the battery state of charge
(SOC) at each instant, to prevent excessive SOC variation from
the intended constant. More remarkably, to assure the vehicle's
charge-sustaining capability, a tangent-shape function of the
SOC deviation, for example, was used to rectify the EF (Tian et
al., 2019). In addition, planning the SOC reference trajectory
could be enhanced by including more factors on top of the travel
distance (e.g. expected demand for power or future average
speed) (Tian et al., 2018). Furthermore, ANNs including neuro-
fuzzy systems and recurrent neural networks (RNN) can be
utilized to produce the SOC reference trajectory depending on
driving data (Han et al., 2020; Montazeri-Gh and Pourbafarani,
2017). Besides, the SOC reference generator promoted by NN
makes use of NN's exceptional learning capabilities, allowing full
utilization of implicit information from optimum SOC reference
trajectories of distinct driving cycles. To get rid of the twofold
faults that cause sub-optimum performance, the EF online
estimating technique should intelligently manage the EF with no
assistance of the SOC reference trajectory, while also ensuring
that the SOC could end at the target value and the optimum fuel
economy. Indeed, the ideal scenario mentioned above can be
realized by utilizing the NN-improved equivalent consumption
minimum strategy (ECMS) driven by data. In an experiment of
Xie et al. (Xie et al., 2018), an equivalent consumption minimal
technique driven by data, employing an ANN to compute the
equivalent factor was described. Accordingly, the NN was
trained with the use of speed profiles in the real world. Based on
the results, the suggested data-driven equivalent consumption
minimal technique outperformed global optimization
approaches such as Pontryagin's minimal principle and dynamic
programming approaches in terms of fuel economy. Apart from
that, the computing time of the suggested technique in
comparison to the total journey duration suggested a high
potential for developing a time-conscious energy control
technique. Also, the obtained findings indicated that the
suggested equivalent consumption minimal method using ANN
created the same fuel economy as global optimization
approaches like the PMP and DP techniques, and it
considerably lowered total energy consumption expense by
24.9%, 17.7%, 29.6%, and 28.7%, for initial SOC levels of 0.65,
0.85, 0.35, and 0.45, in turn in comparison with the charge-
sustaining and charge-depleting (CD-CS) approach based on
rules (Xie et al., 2018).
3.1.1. Neural networks for energy optimization: Distributed energy
resources
Apart from that, Chen et al. [54] presented a novel intelligent
technique that uses dual neural networks (NNs) to adaptively
adjust the equivalent factor to achieve near-optimum fuel
economy. The technique does not require the charge reference
state, and it uses a Bayesian regularization NN to forecast the
near-optimum equivalent factor online, while a backpropagation
NN is used to predict the on/off state of the engine to improve
the forecast quality. Fig. 4c summarizes the design process
sketch and detailed ECMS architecture based on NN. According
to the results of the control performance validation and testing,
the suggested NN-based ECMS was observed to create
equivalent fuel efficiency to the DP optimum solution. Besides,
the suggested technique achieved an average fuel savings of
96.82% of worldwide optimization outcomes overall validating
driving cycles. Moreover, under WVUSUB_7 and CQ2_3, the
proposed approach was projected to save 95.96% and 98.69%
Fig. 3. Workflow for Development, Testing, and Training of ANNs
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of gasoline in turn during driving cycles that have not been
subjected to NN training (Z. Chen et al., 2022).
In an alternative illustration, the estimation of projected
power generation significantly influences the availability of
excess energy for storage or commercialization, in addition to
the potential insufficiency of energy requiring supplementation
from the system. However, solar power generation exhibits
sporadic patterns, rendering continual and precise prediction a
laborious task. Consequently, this challenge serves as a driving
force for researchers to explore the applications of NNs in
energy forecasting. Additionally, the synergistic fusion of neural
networks (NNs) with complementary algorithms represents an
effective approach for enhancing predictive capabilities. By
seamlessly combining the strengths and unique features of
different algorithms, it is possible to enhance predictive
performance and achieve more accurate and robust forecasts.
This integration holds significant potential in optimizing
predictions across diverse domains, ranging from energy
forecasting to weather prediction, and opens up exciting
possibilities for advancing the field of predictive analytics (Liu et
al., 2017) and autoregressive moving average model (ARIMA)
(Duan et al., 2021). More notably, an innovative technique was
provided by Kevin et al. (Förderer et al., 2018) aiming to
represent and communicate distributed energy resources'
energy flexibility. In their experiment, the devices were
combined with ANNs, operating as surrogate models.
Moreover, the flexibility that was represented by an ANN could
be determined by the state of the related devices and their
surroundings, requiring just a little status update to be sent for a
third party to design feasible load profiles. As a result, unlike
other techniques, including support vector data description,
novel ANNs were only required when there was a change in the
device configuration (Förderer et al., 2018). In general, ANN
could also be used to predict solar panel energy output (Eseye
et al., 2018), electricity demand (Chiñas-Palacios et al., 2021a;
M. Kim et al., 2019), and wind speed (T. Liu et al., 2018).
It is not hard to see that one of the primary reasons for
lowering the consumption of energy is the rise in power
demand. Smys et al. (S et al., 2020) attempted to reduce the
energy utilization of the street light system because of its
inefficiency in managing and handling the power flow and
considering current demands on the light intensity. Thus, the
authors proposed a way of managing power to efficiently limit
its consumption through the comparison between the light
intensity and the weather conditions. In the suggested
technique, ANNs were employed to govern the power of
streetlights. Assessing the strategy produced findings resulting
in improved power management and lower power use in street
lighting (S et al., 2020). Huseyin et al. (Yavasoglu et al., 2020a)
discovered that the power split in HESS could be improved by
developing a convex optimization issue to achieve specific
objectives, leading to a 5-year battery lifespan extension.
However, due to the complexity and numerous variables
involved, achieving convex optimization of complex systems
can be challenging, and linearization is not suitable for all
systems. Therefore, to address the challenge of multi-target
energy management, an approach based on neural networks
(NN) was devised and trained using outputs from convex
optimization. The results from simulations demonstrated that
the trained NN model successfully addressed the optimization
problem in 92.5% of the cases where convex optimization was
employed. (Yavasoglu et al., 2020a). Significantly, Yadav et al.
(Yadav et al., 2015) compared various ANN models, including
GRNN (known as generalized regression neural network), n-
ftool (so-called fitting tool), and RBFNN (radial basis function
neural network), aiming to estimate the potential of solar power
sources in India. Accordingly, the n-ftool was recognized for its
ability to accurately estimate the target parameter in a variety
of positions. Moreover, a forecast engine was created by
Abedinia et al. (Abedinia et al., 2018) for estimating solar energy,
based on a metaheuristic optimizer which is known as shark
smell optimization paired with ANN. The researchers proposed
this tool as it outperforms traditional predictors such as
conventional GRNN, RBFNN, ANN, and their wavelet types
Fig. 4. (a) – ANN structure; (b) - Activation functions used in ANN (Natsheh, 2013); (c) - Framework of ANN-ECMS (Z. Chen et al., 2022)
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(normalized root mean square errors (RMSEs). In addition, Yaci
et al. (Yaïci et al., 2017) illustrated the ANN efficacy in modeling
solar power systems, and the influence of issue dimension
(namely the number of inputs) on the accuracy. After the model
was investigated with real-world data, it was concluded that
accuracy decreased progressively as the size was reduced
Table 2
Features and gaps of the considered state-of-art approaches
Ref Technique Model Performance Metrics Identified Research Gaps
(Ahmed et al.,
2020b)
Adaptable energy management model
Machine Learning + Gaussian
Process Regression
Prosumer Energy Surplus,
Prosumer Energy Cost, Grid
revenue
Model fails to consider the ramifications of
uncertainty and demand response, while also
neglecting to ensure the security and privacy of the
data acquired.
(Chou et al.,
2019b)
Hybrid: Linear autoregressive
integrated moving average model and
nonlinear nature-inspired metaheuristic
optimization-based prediction model
Autoregressive integrated moving
average + Metaheuristic Firefly
Algorithm
Root mean square error,
correlation coefficient, mean
absolute error, total error rate
The effects of data sparsity on the efficacy of
machine learning models, and the influence of
model selection on their performance, have not
been focussed.
(Yacim and
Boshoff, 2020)
Rule-based adaptive protection scheme
Artificial neural network + support
vector machine
Accuracy, Correlation
measurement
Utilizing a limited dataset for model training gives
rise to overfitting. The consideration of spatial and
temporal autocorrelation effects on property
valuation models has been omitted.
(Han et al.,
2022)
Adaptive equivalent consumption
minimization strategy
RNN with LSTM trained offline Precision, Qloss
There exists an absence of insight into the variations
in the effectiveness of the LSTM model contingent
upon the chosen domain adaptation strategy.
(Han et al.,
2022)
Improving the forecasting accuracy of
wind speed with strong nonlinearity
and nonstationarity
Data decomposition techniques with
RNN and error decomposition
correction methods
Accuracy
The paper neglects to address the ramifications of
diverse operating conditions on the overall
performance of the method.
(J. Kim et al.,
2019)
Hybrid power demand forecasting
model
(c, l)-Long Short-Term Memory
(LSTM) + Convolution Neural
Network
Accuracy, Kappa statistics
The paper does not take into account the influence
of varying load patterns on the performance of the
model.
(Chiñas-Palacios
et al., 2021b)
Energy demand variations
ANN-based model hybridized with a
Particle Swarm Optimization
Prediction accuracy
This work has a limitation on performance
degradation when the dimensionality of the
parameter space increases.
(H. Liu et al.,
2018)
Short-term wind speed forecasting
Ensemble Empirical Mode
Decomposition (EEMD) + Support
Vector Machine (SVM)
Forecasting accuracy and
management tool
Computationally expensive method
(Yavasoglu et al
.,
2020b)
Multi-objective energy management
with complex utilization of multiple
energy storage units
Neural network (NN)-based machine
learning
92.5% convex optimization
The sensitivity of the Neural Network on the choice
of hyperparameters has not been focussed.
(Jiang et al.,
2020)
Effective forecasting model for
nonlinear and nonstationary solar
power time series
Neural network + metaheuristic
algorithm
Forecast precision
The study does not encompass the impact of
hyperparameter selection on the performance of
neural networks.
(Al Sumarmad
et
al., 2022)
Power flow between renewable energy
sources, storage systems, and their
backup
ANN controllers State Of Charge Computationally expensive method
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(Moayedi and Mosavi, 2021). In general, features and gaps of
the considered state-of-art approaches are given in Table 2.
According to the majority of the research, buildings in
affluent nations contribute 20-40% of the world's energy
consumption (Pérez-Lombard et al., 2008). Buildings utilize
energy throughout their life cycle; however, 80-90% of that
energy is spent during the operating period (Atmaca and
Atmaca, 2015; Praseeda et al., 2016; Ramesh et al., 2010;
Whitehead et al., 2015). As a result, building energy
management systems (BEMS) plays a critical part in this sector
(Doukas et al., 2007). Indeed, BEMS has contributed to
continuously managing the energy of the building (Doukas et al.,
2007), making buildings smarter through real-time automated
control and monitoring (Xiao and Fan, 2014), as well as
optimizing energy consumption (Gangolells et al., 2016).
Therefore, Marcel et al. (Macarulla et al., 2017) outlined the
approach for the implementation of a predictive control method
in a commercial BEMS applied in boilers in buildings, and the
obtained results were also described. The suggested control is
according to a NN which starts the boiler daily at the optimal
moment, depending on the surrounding environment, intending
to attain thermal comfort levels when a working day begins. In
particular, the training patterns were created using testing data
collected from two heating seasons. After that, a variety of NN
structures were examined and the optimal one was utilized to
build and apply the predictive control approach in the current
BEMS. Ultimately, a set of KPIs was employed to evaluate the
effectiveness of the control plan. The block diagram of the NN
implemented in the BEMS was illustrated in Fig. 5. Apart from
that, the Tw, Te, and Ti values were normalized using input
boxes. The control method was evaluated for one heating
season, and the advantages of the suggested control technique
were assessed using a set of primary performance parameters.
According to the findings, predictive control being utilized in a
BEMS for boilers in the building could lower the energy needed
for heating the building by roughly 20% while maintaining
comfort for the users (Macarulla et al., 2017).
3.2. Fuzzy logic for energy management through intelligent systems in
hybrid energy and microgrid systems
It is obvious that the exploitation of renewable sources of
energy has enormous promise for various applications, and off-
grid stand-alone systems particularly bring several advantages.
The entire system is known as a HES (hybrid energy system)
since it combines at least one renewable resource with one extra
resource and one storage factor. Additionally, a proper EMS
must be created to govern the power flow among the parts of a
HES. The EMS is often a centralized controller which controls
all of the components. As a result, the hybridization level of the
HES increases the complication of building an EMS.
Furthermore, if there is any change in the configuration of HES,
such as when one component withdraws due to a defect or
maintenance, the central controller is incapable of adjusting its
reaction. Apart from that, in case a new factor is introduced to
an EMS with a central controller, it is necessary to modify the
EMS. Thus, it is intriguing to determine a dependable, flexible,
scalable, and open EMS. More noticeably, a novel method for
HES based on multiagent system technology (MAS) was
developed by Jérémy et al. (Lagorse et al., 2009), in which HES
was viewed as a collection of autonomous entities that
collaborated rather than a global system to govern. According
to the above-mentioned key characteristics, intelligent element
and MASs technology is predicted to fundamentally transform
how complicated, open, and distributed systems are designed
and deployed. Because of the dispersed, open, and complicated
features of HES, MAS technology seems to be a suitable answer
for energy management in HES. Additionally, an HES may be
considered a collection of "intelligent" and autonomous factors
that can adapt to situations in their environment using an agent-
based method (Lagorse et al., 2009). Roiné et al. (Roiné et al.,
2014) described an EMS in which the FLC analyses the
evolution of pricing over a single day, the production, the
demand for energy, and the time of day to provide an
economical grid. Besides, scenarios with more degrees of
freedom were also taken into account in other works, in which
the EMS governs distinct storage factors, controllable, or even
a combination of both factors mentioned above aiming to
conduct demand side management and DR approaches
(Pascual et al., 2014; Tascikaraoglu et al., 2014; Wang et al.,
2014). In this scenario, the control systems utilized are often
complex, such as MPC (Model Predictive Control), and
encompass both production and demand prediction (Prodan
and Zio, 2014)(Bruni et al., 2015). Barricarte et al. (Barricarte et
al., 2011) proposed an EMS design based on heuristic
knowledge of the wanted micro-grid behavior, in which the
amount of power attributed to the storage system and the grid
is calculated using adjustable analytical expressions related to
the power balance between production and consumption, along
with the battery SOC serving as major variables. The
aforementioned heuristic knowledge suggested employing FLC
to build the EMS for the instance under investigation, because
this technique readily incorporates the user's experience instead
of utilizing a system's mathematical model (Fossati et al., 2015;
Mohamed and Mohammed, 2013; Passino et al., 1998).
Furthermore, using the identical input variables (Barricarte et al.,
2011), the researchers showed that the FLC creation with only
25 rules moderately enhanced battery SOC as well as the grid
power profile achieved in (Aviles et al., 2012)(Barricarte et al.,
2011)(Arcos-Aviles et al., 2018). More importantly, Diego et al.
(Arcos-Aviles et al., 2018) designed a minimal complexity FLC
with just 25 rules to be incorporated in an energy management
system, applied in a home grid-connected micro-grid with
renewable sources of energy and storage ability. The major
purpose of this design is in order to retain the battery state of
charge in safe limits while minimizing the fluctuations of the grid
power profile. It is noted that rather than relying on predictions,
the suggested methodology employed not only the battery state
of charge but also the microgrid energy rate of change for
Fig. 5. Diagram of implemented neural network applied to
building energy management systems (Macarulla et al., 2017)
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raising, reducing, or maintaining the power absorbed or
delivered by the mains.
3.2.1. Comparative analysis of microgrid energy management
Strategies
Bogaraj et al. (Bogaraj and Kanakaraj, 2016) showed an
energy management system for microgrid systems based on
intelligent multi-agent systems. This system maintains the
balance of power between sources of energy and loads by
utilizing forecasts of PV production, load demand, and wind
production to provide the needed load. In addition, another
MAS was presented by (Aung et al., 2010), applied in a microgrid
system aiming to obtain optimal dispersed source utilization
with the highest level of output from renewable sources and the
lowest diesel use. Furthermore, Boudoudouh et al. suggested a
multi-agent system for microgrid energy management,
described in (Boudoudouh and Maâroufi, 2018). The
simulations were carried out with the help of Java Agent
Development and MATLAB-Simulink tools. This model's
dependability was proven by meeting needs like autonomy and
adaptability in a way that any modifications would not break the
entire control method system. Aside from that, Logenthiran et
al. (Logenthiran et al., 2012) studied a multi-agent system
towards the microgrid's real-time operation, proposing an
operational approach concentrated on production scheduling
and demand-side control. The research described above also
highlighted the usefulness of multi-agent systems when applied
in microgrids. Notably, in this study, the MAS technique was
employed to create an energy control system for microgrid
systems that is based on the maximizing of renewable
resources, and the bidirectional DC or DC converter was
handled by ANN controllers. Besides, Aiman et al. (Albarakati et
al., 2021) suggested an EMS based on maximizing energy
exploitation from renewable sources by operating them in
Maximum Power Point Tracking conditions. Furthermore, the
stored energy was managed by applying ANN controllers to
optimize battery discharging and charging. The primary goal of
this system is so as to retain the balance of power in the
microgrid as well as to give a flexible and configurable control
for various situations with all variation types (Albarakati et al.,
2021).
In fact, because of the intermittent and stochastic
character of deeply penetrated renewable sources of energy and
demand, efficient multi-energy management in a microgrid is
considered a difficult issue. Thus, to tackle this hindrance, it is
necessary for the energy management system to frequently
employ day-ahead energy planning based on prediction and
real-time energy distribution for successfully coordinating the
operation of dispatchable elements, such as thermal units and
energy storage based on battery. Also, an adaptive optimum
energy management solution based on fuzzy logic was provided
by Dong et al. (Dong et al., 2021) for adaptively developing
suitable future fuzzy rules for dispatching energy in real-time in
the context of operational uncertainty. It is noted that real-time
energy distribution depending on optimum fuzzy logic rules
established may then be conducted to fulfill different
operational objectives, such as minimum cost of operation and
lowest power fluctuation. The suggested technique was
thoroughly tested in simulation trials against two current
methods, including the dispatch technique based on online rule
and the offline scheduling approach based on meta-heuristic
optimization (Dong and Sharma, 2023). According to the
numerical findings, the presented energy management
approach was proved to outperform others (Dong et al., 2021).
More remarkably, Deepak et al. (Jain et al., 2022) created energy
management based on fuzzy logic and FLEM-TFP for smart
transport systems using Cyber-Physical Systems. The presented
FLEM-TFP system consists of two major processes, including
Traffic Flow forecast and energy management. More
interestingly, the engine torque required is also computed using
an ANFIS (known as adaptive neuro-fuzzy inference system)
model based on a variety of measurements. Furthermore, in
intelligent transportation systems, an SFO-based FWNN
technique is utilized to predict traffic flow. The trials revealed
that ANFIS-FFA has brought good results, with an average TFC
obtained being 25.98, which is significantly lower than the
values achieved by the other approaches. In addition, it is clear
to see from afore-mentioned data that the proposed method
could increase not only energy efficacy but also total fuel
economy. In the future, the provided model might be used to
create methods for providing dynamic resources in an
intelligent transport systems environment for Cyber-Physical
Systems (Jain et al., 2022). The tabulation of Table 3 presents a
comprehensive comparative analysis of the features examined
by the prior scholarly review articles.
3.3. Support vector machine (SVM) in energy regulation
Support vector machine (SVM) has been employed as an
artificial intelligence model and is a well-known supervised
machine learning approach to classify (Novitasari et al.,
2023)(Kusnawi et al., 2023). Also, it is applicable to not only
classification but also regression difficulties. Indeed, the core
idea of SVM is to transfer input characteristics to a higher-
dimensional plane (Karaağaç et al., 2021). More notably, the
kernel function simplifies the learning process by transferring
non-separable data in input data space to data that can be
Table 3
Comparative analysis of intelligent energy prediction surveys
Literature
Energy
efficiency
Energy demand
prediction
Data-Driven
methodologies
Energy
modeling
Control
mechanisms
Energy
management
(Khan et al., 2020)
x
-
-
-
-
(Tabanjat et al., 2018)
-
-
-
-
-
x
(Tascikaraoglu et al., 2014)
-
x
x
-
-
x
(Pascual et al., 2014)
-
x
-
-
x
x
(Prodan and Zio, 2014)
x
x
x
x
x
(Bruni et al., 2015)
-
x
-
-
x
x
(Paudel et al., 2017)
x
-
x
x
-
-
(Zendehboudi et al., 2018)
-
x
-
x
-
-
(Sameti et al., 2017)
-
-
x
-
-
-
(Houssein, 2019)
-
-
x
x
-
-
(Runge and Zmeureanu, 2019)
x
-
-
x
-
x
(Elsheikh et al., 2019)
-
-
-
x
-
x
This review strategy
x
x
x
x
x
x
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separated in a higher dimensional one (Ağbulut et al., 2020;
Paudel et al., 2017). Moreover, SVM is regarded as among the
finest machine learning techniques for both regression and
classification, according to some statistical learning theories
(Gao et al., 2003; Yuan et al., 2010). When the results of SVM
were compared to those of other strong data-driven empirical
techniques like ARIMA, RBF, MLP, and IIR-LRNN, the SVR
results were observed to exceed or be equivalent to those of
other learning machines (Erfianto and Rahmatsyah, 2022;
Moura et al., 2011; Said et al., 2023). Additionally, SVR is thought
to function well for time series analysis because of better
generalizability and the capability of ensuring a global minimum
for certain training data (Fuadi et al., 2021; Wu et al., 2004).
For the link between the variable and the goal value, Sai et
al. (Sai et al., 2020) employed an SVM with enhanced fitting and
inserted the fitting forecast model into the response surface
approach. Following collaborative analysis, the model was fed
into a non-dominated sorting genetic algorithm-II. In addition,
following the optimization operation, the optimum working
conditions for enhancing the operating efficacy of the solar
membrane distillation system were obtained, allowing open-pit
mine prosumers to conduct smart management of producing,
storing, and consuming solar energy at the same time (Sai et al.,
2020). In a study by Azam et al. (Fuadi et al., 2021), electricity
usage was forecasted as part of the intelligent power grid
development and electrification network information
enhancement with the goal of performing energy management.
Also, an SVM was utilized in the research to estimate electrical
loads and compare the results to measurable electrical loads. In
comparison with industrial, commercial, or residential electrical
loads, laboratory electrical loads had unique features. Besides,
RMSE was used for result prediction at various levels of trust or
accuracy. The attained prediction technique had MSE = 0.14,
MAE = 0.21, and RMSE = 0.37, indicating that SVM might be a
useful tool for managing energy (Fuadi et al., 2021). It is noted
that the microgrid dispatch's optimization is obtained by using
data from renewable energy generation and load predictions in
microgrids. Consequently, energy forecasting is critical in the
electrical industry. Also, accurate prediction of power load is
crucial for lowering the consumption of energy, decreasing
power generating costs, and improving social and economic
benefits (Khan et al., 2020). A number of approaches have been
employed to forecast wind and solar energy supplies. In terms
of predicting, the SVM modeling technique was demonstrated
to have higher effectiveness compared to other modeling
methods as the SVM is fast, simple to use, and gives accurate
results. According to research based on significant analysis,
SVM models can yield much greater accuracy in comparison
with other models (Zendehboudi et al., 2018). Meanwhile,
according to a study by Ehab et al. (Issa et al., 2022), SVR is a
regression model utilized for optimizing. In fact, SVR is known
as a form of SVM that could learn regression functions and is an
SVM classification technique extension. Thus, Improving the
accuracy of energy projections is necessary for the electrical
grid to operate more effectively (Issa et al., 2022). More
intriguing, the accuracy of SVM, a prominent machine-learning
technique for simulating solar radiation was investigated and
proved by Meenal and Selvakumar (Meenal and Selvakumar,
2018). When used with an ideal set of data, the strategy
mentioned above showed superiority over the empirical
methods and ANN for this goal. Aside from that, Quej et al. (Quej
et al., 2017) researched the capabilities of SVM, ANN, and
ANFIS in replicating sun radiation daily, with average
correlations of 0.689, 0.652, and 0.645 respectively for the top
models, so the SVM has been considered the most trustworthy
predictor (Moayedi and Mosavi, 2021).
3.3.1. Comparison of SVMs and ANNs for energy forecasting
Some professionals researched assessment rules, energy
regulatory system model creation, system state forecast, and
the right combination of the energy regulatory system and AI
(Zhu et al., 2020)(Armin Razmjoo et al., 2019). Moreover, the
energy regulating system's overall performance was measured
by Yan et al. (Yan et al., 2020a) with the employment of an
analytic data model. The purpose of applying this model was to
investigate the link between the state change of a certain energy
sort and the overall regulatory state. Ultimately, the design
experiment validated the method's position in studying the
energy regulation system's data perception (Yan et al., 2020b).
Furthermore, based on data mining, the authors suggested an
enhanced SVM method. It might considerably take advantage
of sensing information acquired by intelligent devices based on
the rough identification of the energy supervision system's data
status. Zhu (Zhu, 2021) studied the e-commerce energy
regulatory system model employing data mining and the SVM
technique. The experimental study demonstrated that the
updated SVM technique could achieve objective regulatory
efficiency assessment based on data exploitation and might
result in the best method depending on scenarios in the actual
application phases of the energy supervision system.
Accordingly, the performance was observed to be good,
suggesting that the energy supervision system could achieve
above 97%, which was greater than the majority of the most
recent techniques (Zhu, 2021). Low-energy buildings have been
viewed as a viable alternative for the construction environment
in order to meet high energy efficacy criteria. Nevertheless, in
comparison to traditional buildings, low energy buildings add a
significant time constant, which slows down the heat transfer
rate between the building interior and the outer environment
and at the same time, adjusts the inside climate albeit rapid
changes in climatic circumstances. As a result, Subodh et al.
(Paudel et al., 2017) emphasized an AI model to estimate the
energy usage of buildings with the use of SVM. According to the
numerical findings, the "relevant data" modeling strategy,
depending on limited representative data selection, predicted
heating energy demand more accurately (R2 = 0.98; RMSE =
3.4) compared to the "all data" modeling method (R2 = 0.93;
RMSE = 7.1) (Paudel et al., 2017). In an investigation conducted
by Sai et al. (Sai et al., 2020), an upgraded SVM was employed
and the fitting prediction model was inserted into the response
surface approach for the relation between the desired value and
the variable. Interestingly, a set of optimum operating
conditions for the solar membrane distillation system could be
achieved after the optimization using SVM fitting as well as an
NSGA-II multi-goal optimization technique. In particular, the
cold-end cooling water flow was 194.14 L/h, the hot-end feed
temperature was 65.76C, the membrane area was 0.03 m2 and
the hot-end feed flow was 171.56 L/h. In addition, the
researchers also discovered that the optimum operating
conditions were gained after the operation of optimization
aiming to promote the operating efficacy of the solar membrane
distillation system, allowing open-pit mine consumers to
smartly manage production, storage, and consumption of solar
power at the same time (Sai et al., 2020). More noticeably,
Kaytez et al. (Kaytez et al., 2015) examined regression analysis,
SVM, and ANN forecasting accuracy for predicting the
consumption of power in Turkey. It is noted that total power
production, population, total number of customers, and installed
capacity were utilized as inputs while total electricity
consumption was employed as output, with the use of data
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during the period of 1970-2009. When the findings were
compared, the MAPE of the LS-SVM experiment results was
1.004%, while 1.19% was attained for the ANN, and 3.34% for
the statistical regression analysis model. Besides, Ogcu and
Demirel et al. (Oğcu et al., 2012) predicted power consumption
in Turkey using ANN and SVM, and they spent two years
creating models based on monthly energy use data. The MAPE
utilized by the SVM and ANN for the test set of data was 3.3 %
and 3.9 %, in turn (M. Shao et al., 2020). Indeed, there is no
intrinsic approach in SVMs and NNs for specifying the states
and related methods. The above-mentioned factors could
account for the reason why SVMs and NNs have been preferred
for energy prediction rather than energy control. Furthermore,
SVMs and NNs both contain numerical parameters that could
be changed, which could influence how well they function.
Attempting to manually modify the settings, on the other hand,
is not practical. Significantly, iterative tuning of the model might
be accomplished by the employment of optimization methods,
including Cuckoo Search Algorithm (T. Liu et al., 2018), Particle
Swarm Optimization and Grasshopper optimization algorithm
(Chiñas-Palacios et al., 2021a; Eseye et al., 2018; Veza et al.,
2022b; Zhang et al., 2023), Genetic Algorithm (Sameti et al.,
2017), and Dragonfly Algorithm (Li et al., 2023; Zhang et al.,
2019).
3.4. Reinforcement learning and metaheuristic algorithms
Noticeably, a number of research have looked at real-time
dispatch methods for energy management to deal with the
effects of stochastic properties and forecast mistakes. Based on
smart model-free learning approaches, the RBC
(Venayagamoorthy et al., 2016; Yazdanian and Mehrizi-Sani,
2014) was created for optimum management and control of the
system. Interestingly, the Lyapunov optimization was employed
in the online EMS with constraint relaxation in the investigations
(Shi et al., 2017; Yan et al., 2019). The resolutions mentioned
above frequently examine only the present operational states of
the system and frequently simplify the operational requirements
to facilitate real-time calculation. Hence, effective energy
management is difficult to achieve over the long run. In addition,
Markov decision processes (MDPs) may be in use for optimizing
real-time energy dispatch. Based on the equation of Bellman for
decomposing temporal dependency, DP and ADP (Zeng et al.,
2019) can be employed to handle such a stochastic sequential
choice issue repeatedly. Besides, RL has recently been regarded
as a potential technique for solving MDPs efficiently (W. Liu et
al., 2018). In another study of Zhang and Sun (Zhang and Sun,
2016), they created a consensus transfer Q-learning algorithm
with the aim of energy dispatch which shared Q-value matrices
and used previous information to accelerate algorithm
convergence. For dynamic economic dispatch, (Dai et al., 2020)
suggested an RL method in which state-action-value function
approximation was integrated with multiplier distributed
optimization based on splitting. Nonetheless, to prevent
prohibitive computational complications because of the high-
dimensional state space, the aforementioned methods
frequently need feature characterization and complex learning
rules (Dong et al., 2021; Mnih et al., 2015). As reported, heuristics
and Bayesian networks were also utilized to manage energy.
Regarding heuristic algorithms, they are known as a form of
algorithm based on the search that seeks the best solution to a
specific issue (Desale et al., 2015). They have been utilized in the
literature to optimize EV charging schedules (Vasant et al.,
2020), the energy consumption of cooling systems in a building
(Ikeda and Nagai, 2021), trading portfolios for electricity
markets (Faia et al., 2017), and energy resource utilization in a
microgrid (Bukar et al., 2022). Indeed, heuristics are valuable
because they can provide potential answers to issues for which
there is no obvious answer (Ali et al., 2023). Moreover, some
factors such as EV scheduling and the utilization of energy
resources are affected by elements that are not always under
control. As a result, Heuristics can present a viable solution that
can be assessed. However, it might not be common since
understanding the way to employ it in an energy management
AI can be challenging. Whilst RL and FL algorithms instantly
produce an action that can be employed immediately, heuristics
search for resolutions (Li et al., 2023).
Speaking of Bayesian Networks, they are graphs supporting
the description of the possibilities of events happening based on
the present state (Horný, 2014). In the document, Bayesian
Networks have been utilized for user response prediction to
demand side management measures (Z. Shao et al., 2020), for
detecting prospective variations in electricity markets (Roje et
al., 2017), and taking into consideration the uncertainty in
energy usage and solar PV energy generation (Sun et al., 2020).
It is not hard to see that Bayesian networks are valuable in
managing energy because they are capable of quantifying
uncertainty, as well as the production of renewable energy
might be intermittent, and user schedules can alter. It is
noticeable that Bayesian Networks can be unpopular since, like
Heuristics, applying Bayesian Networks in an energy
management AI could be difficult. The Bayesian Network
provides a map of probabilities; however, how to teach an AI to
assess those probabilities is such an issue (Li et al., 2023).
Furthermore, metaheuristic algorithms have opened a new path
for more powerful predicting models based on the skeleton of
traditional tools such as ANFIS and ANN (Bakır et al., 2022). The
methods mentioned above are commonly utilized for analyzing
renewable energy (Corizzo et al., 2021; Houssein, 2019), such as
solar energy (Bessa et al., 2015), wind power (Cavalcante et al.,
2017; Liu et al., 2019), and, more specifically, solar energy-
relevant simulations (Akhter et al., 2019; Elsheikh et al., 2019).
More importantly, to avoid concerns such as local minima, such
approaches (namely metaheuristic-based hybrids) give ideal
parameters for the core prediction technique (Moayedi et al.,
2019). Several researchers studied hybrid metaheuristic
techniques to improve algorithm performance. Several of the
above-mentioned hybrid algorithms include the many-objective
optimization model (Cao et al., 2020d, 2020a, 2020b, 2020c), the
whale optimization algorithm (Tu et al., 2021; Wang and Chen,
2020), moth-flame optimization (Shan et al., 2021; Wang et al.,
2017; Xu et al., 2019), grey wolf optimization (Hu et al., 2021;
Zhao et al., 2019), harris hawks optimization (Chen et al., 2020;
Zhang et al., 2021), global numerical optimization, bacterial
foraging optimization (Xu and Chen, 2014), Monarch Butterfly
optimization (Bacanin et al., 2020), the grasshopper optimization
algorithm (Yu et al., 2022), multiobjective 3-d topology
optimization (Cao et al., 2020e), fruit fly optimization (Shen et al.,
2016), topology optimization (Fu et al., 2020), the fuzzy
optimization method (Chen et al., 2019; Wasista et al., 2023), and
data-driven robust optimization (Moayedi and Mosavi, 2021; Qu
et al., 2021).
3.4.1. Comparison of different meta-heuristic optimization algorithms
In general, energy management in smart grids has common
goals such as minimizing electricity expenses, maximizing user
comfort, lowering PAR, integrating renewable sources of
energy, and reducing aggregated power usage. A lot of demand-
side management approaches have recently been introduced to
attain the aforementioned targets. Besides, non-integer linear
programming, mixed integer linear programming, convex
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programming, and mixed integer non-linear programming are
in use for reducing costs and energy usage (Molderink et al.,
2009; Soares et al., 2011; Sousa et al., 2012; Tsui and Chan,
2012). These systems, however, cannot manage huge quantities
of equipment. Hence, distinct meta-heuristic optimization
strategies can be utilized for managing energy in smart meters
to address the shortcomings of the aforementioned
methodologies. For instance, some researchers employed a
genetic algorithm aiming to minimize power costs (Arabali et al.,
2013; Zhuang Zhao et al., 2013). In addition, demand response
as well as ant colony optimization was utilized to cut down
electricity bills and the use of aggregated power (Liu et al., 2011;
Tang et al., 2014). It is obvious that the majority of energy is
utilized in residential areas, and it is continually increasing,
which has drawn the attention of scientists to household
appliance scheduling. Zafar et al. (Zafar et al., 2017) assessed the
performance of a home energy management system with the
use of three meta-heuristic optimization approaches: harmony
search algorithm, enhanced differential evolution, and bacterial
foraging optimization, to minimize electricity expenses,
consumption of energy, and lower peak to average proportion
while maximizing the comfort of users. The findings of their
simulation revealed that there is a trade-off between the
expenses and the user's comfort. Also, the findings
demonstrated that the harmony search algorithm outperformed
other approaches in terms of costs (Zafar et al., 2017). In another
study, Galván et al. (Galván et al., 2017) took advantage of a
multi-objective PSO approach to optimize the SE modeling
intervals, and they also created a nonlinear technique
employing ANN, and their results indicated the PSO optimizer's
great applicability for the given target. In addition, two
metaheuristic approaches were utilized in an experiment by
Zhao et al. (Zhao et al., 2020) to forecast the compressive
strength of concrete, including shuffled complex evolution and
teaching and learning based on optimization. Similarly, this
technique was also effectively employed by Halabi et al. (Halabi
et al., 2018), in conjunction with an ANFIS system to
approximate solar radiation every month. Meanwhile, Vaisakh
et al. (Vaisakh and Jayabarathi, 2022) proposed a mixture of two
approaches for modifying the structure of different ANNs used
in SIr forecasting, namely the grey wolf optimization and the
deer hunting optimization algorithm. According to the results
obtained, the introduced optimizer achieved promising
enhancement. Furthermore, Louzazni et al. (Louzazni et al.,
2018) demonstrated the firefly algorithm's capability aiming to
assess the photovoltaic system's parameters under various
scenarios. In comparison to prior utilized metaheuristic
algorithms, the firefly algorithm was reported to produce more
trustworthy and valid results when adjusting photovoltaic
parameters. More interestingly, Bechouat et al. (Bechouat et al.,
2017) proved the efficacy of the PSO and GA for the same target.
Whereas, Abdalla et al. (Abdalla et al., 2019) effectively applied
wind-driven optimization to the optimum power monitoring of
photovoltaic systems (Moayedi and Mosavi, 2021). The major
applications encompass load demand profiling, energy
prediction, controlling techniques, state of charge in EVs,
consumption minimum strategy, and charge-sustaining
depleting approaches. The articles are classified and arranged
based on these application scenarios of ANN, and an extensive
comparative analysis of the features considered by these articles
is presented in Fig. 6.
The target of the agent in RL is to maximize or minimize a
value. This value might represent energy expenses or the
consumption of energy in the context of energy management.
An RL algorithm constantly alters its operations in response to
environmental feedback. Besides, unsupervised learning (or UL
for short) approaches are associated with recognizing important
patterns in data and clustering them after that, based on the
patterns identified above. Therefore, they are valuable in
categorization difficulties. Since it is not easy to apply data
clustering to energy management, unsupervised learning tends
to be less common (Jo, 2021)(Li et al., 2023). Indeed, RL is a
subfield of machine learning research in which an agent learns
itself what behaviors to perform in a given environment to
maximize the reward (Barrett and Linder, 2015). More
interestingly, this is often related to a large amount of error and
trial from an agent when it learns the greatest reward can be
achieved from which actions. Apart from that, the algorithm is a
general pseudocode that outlines the major phases of a normal
RL algorithm (Mason and Grijalva, 2019). Notably, model-free
and model-based RL algorithms are the two types of RL
algorithms. Additionally, Dyna, Explicit-Explore-Exploit,
Queue-Dyna, and Prioritized sweeping are examples of
algorithms based on the model. Whereas, it is unnecessary for
model-free techniques do create an environment model. Many
commonly employed RL algorithms, such as SARSA and Q
Learning, are known as model-free. In particular, Q Learning
(Barrett and Linder, 2015) is considered among the most well-
known RL algorithms. Indeed, it is a model-free and off-policy
reinforcement learning approach, in which off-policy agents
learn the value of their policies independently of their actions
(Barrett and Linder, 2015; Mason and Grijalva, 2019).
3.4.2. Reinforcement learning techniques for intelligent energy
management
More importantly, the optimization framework depends on
reinforcement learning using the Q-learning approach. This
strategy motivates learning via the use of rewards or penalties
based on a series of actions in response to setting dynamics
(Panait and Luke, 2005; Sutton and Barto, 2018). Moreover, in a
deterministic scenario, the approach can determine the most
potential series of actions for a certain environment state, but in
a stochastic one, it can account for the uncertainty in
environment exploration (Panait and Luke, 2005). By
decreasing power consumption, the Q-learning technique has
been proven to obtain great performance in terms of managing
the dynamic power of embedded systems (Prabha and Monie,
2007; Tan et al., 2009). Furthermore, the method has also been
Fig. 6. Segregation of articles based on the application scenario
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used for creating a complete and advantageous demand
response model for power pricing (Yousefi et al., 2011)(Mason
and Grijalva, 2019). Particularly, a retail energy supplier utilizes
Q-learning to establish appropriate real-time pricing while
taking into account various factors like price limits and
consumer replies. For intelligent energy management, the Q-
learning technique can be combined with other methods,
including Metropolis Criterion-based fuzzy Q-learning (Li et al.,
2012), and genetic-based fuzzy Q-learning (Kuznetsova et al.,
2013; Xin et al., 2012). More interestingly, it was discovered that
the combination technique outperformed either MPC or Q-
learning alone (Liu and Henze, 2006). Barrett et al. (Barrett and
Linder, 2015) used Q-learning for the issue of HVAC control in
conjunction with Bayesian Learning for predicting occupancy in
2015. Based on the results, a 10% enhancement was observed
in energy savings over a programmed control system. Besides,
in 2017, deep NN and Deep RL were employed by Wei et al.
(Wei et al., 2017), aiming to solve the HVAC control problem,
and reported energy savings increases of 20-70% above
standard Q-learning. Meanwhile, Chen et al. (Chen et al., 2018)
used Q-learning to regulate the window systems and HVAC. As
reported, the two buildings studied saved 13% and 23% on
energy and reduced discomfort levels by 62% and 80%. Fitted
Q Iteration was applied in an investigation by Reymond et al.
(Reymond et al., 2018) in 2018 for learning to schedule a variety
of domestic equipment, such as dishwashers, water heaters, and
heat pumps. Their findings showed that autonomous learning
outperformed the centralized learning method by 9.65%. As for
managing residential batteries, Wei et al. (Wei et al., 2015)
developed a dual iterative Q-learning technique, and in
comparison with the baseline, a 32.16% reduction was observed
in energy expenses. In addition, Guan et al. (Chenxiao Guan et
al., 2015) employed temporal difference learning to aim to
manage the battery energy storage with PV panels in the
research in 2015. It is noted that temporal difference learning
was found to reduce 59.8% of energy expenses. More
remarkably, Rayati et al. (Rayati et al., 2015) applied Q-learning
to residential energy management in the context of PV
installation and energy storage. When determining the best
control regime, this research took into account household
comfort and CO2 emissions. According to the authors, maximal
energy savings reached 40%, along with a 17% decrease in peak
load, and a 50% reduction in CO2 societal expenses. Remani et
al. (Remani et al., 2019) used Q-learning to schedule numerous
household equipment like lights, dish washers, laundry dryers,
and so on. Aside from that, the authors also constructed a
demand response system based on price, in which a PV panel
was incorporated, indicating a 15% reduction in daily energy
expenditure. Wen et al. (Wen et al., 2015) suggested an energy
management system for demand response for small buildings,
allowing for automatic device scheduling to deal with variations
in electricity prices. Furthermore, Mocanu et al. (Mocanu et al.,
2019) utilized DQL and DPG to improve the system of energy
management for 10, 20, and 48 households in the 2018 research.
In addition, this investigation looked into the employment of
vehicles running on electricity, PV panels, and appliances in the
building. As reported, DPG saved 27.4% on power and DQL
saved 14.1%. Moreover, the researchers employed Q-learning
to exploit the projected 65% potential energy savings for small
houses through effective device scheduling, and they
demonstrated enhancements according to the baseline. Also,
inverse reinforcement learning was applied by Bazenkov et al.
(Bazenkov and Goubko, 2018) to forecast consumer appliance
consumption, and it was observed that IRL outperformed other
machine learning approaches like random forest. In a study by
Jiang et al., a hierarchical multi-agent Q-learning technique was
implemented in a microgrid for responding to the dynamic
demand as well as manage distributed energy sources (Jiang
and Fei, 2015). According to this study, the entire community's
energy expenses were reduced by 19%.
4. Existing limitations and perspectives
AI models offer numerous benefits, but they also have
certain drawbacks. First, AI models and clever algorithms, like
other models driven by data, perform poorly beyond their
training range. Therefore, models are restricted to the value
range encountered during training. As a result, these retraining
strategies can support making sure that AI models efficiently
adapt to novel data and circumstances (Barkah et al., 2023).
Furthermore, AI models are black-box-based models
themselves, so the internals are unknown. They may give a
competent forecasting tool, but they lack comprehension of the
fundamental characteristics of energy use as well as its
behavior. More importantly, the employment of hybrid grey-box
models is considered one way to address this. In the
aforementioned models, AI models are often integrated with
equations based on physics to maximize the benefits of both
models while minimizing their drawbacks. Overfitting is another
constraint that might impair the effectiveness of AI models as
well as smart algorithms. Indeed, overfitting happens when a
model learns too much noise from the training data. To
overcome this challenge, there are several strategies both within
and outside of training to boost generality. Moreover, models
may be trained using a suitably large data collection concerning
the quantity of inputs (MathWorks, n.d.). Although additional
ways exist to assist in ensuring generality, the methods
discussed above offer a concise summary of some possible
approaches to tackle the specific problem. Furthermore,
insufficient hyperparameter selection can result in models with
poor performance in predicting and/or needing more time to
generate estimation, which is another restriction of AI models.
Regardless, in case the hyperparameters of AI models are
properly adjusted, intelligent algorithms along with AI models
can show great performance and short processing times. Hence,
professionals are now required while building AI models (Runge
and Zmeureanu, 2019). Furthermore, delays are thought to have
a significant impact on system operation. Because latency
propagates via a system, an EMS's response is restricted by the
slowest connection in the system. Interestingly, while multi-
stage and hybrid AI models are effective and innovative, their
real-time performance is questionable and needs research. It is
suggested that researchers consider doing mock experiments
with energy systems on a small scale to assess the effectiveness
of the AI model when systems, controller software, and sensors
from other energy resources are taken into account. Indeed, the
simple and effective combination of AI could be a significant
innovation of the concept. Experiment results that have been
validated can guide the future of AI employment in the energy
field. Although AI models have been evaluated in a confined
setting, more research and effort are required when AI is
gradually integrated into a wider system. This results in the core
problem with Energy Management Systems, namely real-time
operation (Li et al., 2023). However, there exist restraints to
utilizing intelligent algorithms in energy management systems
because the vast number of publications describe the
employment of intelligent methods in simulated versions of
energy management issues. It is noticeable that because
intelligent algorithms are known as online learning algorithms,
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they might be used in a physical-based energy management
system with no need to learn in a simulated setting. Hence,
intelligent algorithms must learn effective control policies,
which reduce energy expenses through error and trial. Apart
from that, for this purpose, accurate simulators are required for
the intelligent algorithms agent to learn which rules are optimal
through simulation. When the aforementioned pre-trained
agents are deployed in physical systems, they can further
improve their policies (Mason and Grijalva, 2019).
Significantly, due to the increasing volume of data acquired
by sensors in the future, it is essential to apply deep intelligent
algorithm approaches aiming to build successful policies while
dealing with settings with extremely huge state-action spaces.
Besides, using various variants of classic intelligent algorithms
can be a new pathway for future study in intelligent algorithms
for the management of energy. Meanwhile, several energy
management challenges can be multi-objective intelligent
algorithm issues. Future studies may also investigate the use of
meta-learning to overcome the challenges of intelligent
algorithms in energy management. Furthermore, several future
research questions have been raised, like how long the
intelligent algorithm agent must spend relearning new policies
in those situations. More interestingly, another possible future
study topic can be experimentally comparing various algorithms
as well as other control algorithms. Indeed, further studies might
look towards merging several intelligent systems to control
energy (Mason and Grijalva, 2019). Table 4 presents the
tabulated results of identified constraints and potential
solutions, along with key observations stemming from an
extensive survey and Fig. 7 illustrates the present focus of
research and the potential paths for future research in the field
of AI for Energy Data Analytics.
Table 4
Summary of identified constraints, solutions, and observations from the Intelligent Energy Prediction Survey
Key Points
Inferences
AI Model Limitations
• AI models and algorithms have limitations beyond their training range
• These models are constrained by the value range encountered during training
• Retraining strategies are used to ensure adaptability to novel data and circumstances
• Hybrid grey-box models integrate AI models with physics-based equations to enhance understanding
and performance.
Black-Box Nature of AI
Models
• AI models are black-box-based and lack comprehension of fundamental energy use characteristics
• Hybrid models combine AI and physics-based equations to maximize benefits while minimizing
drawbacks
• This approach enhances forecasting competence and improves understanding of energy behavior
Overfitting Challenge
• Overfitting occurs when a model learns noise from training data
• Strategies within and outside training are used to address overfitting and boost generality
• Adequate data collection and proper hyperparameter selection are crucial to ensuring model
effectiveness
Hyperparameter
Selection
• Poor hyperparameter selection can result in models with poor performance and longer processing times
• Properly adjusted hyperparameters enable AI models and algorithms to achieve high performance and
short processing times
• More technicalities are required to build effective AI models.
Real-Time Performance
• Delays impact system operation, with an EMS's response limited by the slowest connection
• Multi-stage and hybrid AI models are innovative but raise questions about real-time performance
• Mock experiments with energy systems are suggested to assess AI model effectiveness in real-world
scenarios.
AI Integration into
Energy Systems
• AI models have been evaluated in confined settings, but more research is needed for gradual integration
into wider systems
• Challenges exist in utilizing intelligent algorithms due to the predominance of simulated versions in
publications
• Intelligent algorithms need to learn effective control policies through trial and error.
Deep Intelligent
Algorithm Approaches
• Increasing sensor data volume calls for deep intelligent algorithms to develop successful policies in
complex state-action spaces
• Exploring variants of classic intelligent algorithms is a potential pathway for future research in energy
management.
Multi-Objective
Intelligent Algorithm
Issues
• Some energy management challenges are multi-objective intelligent algorithm issues
• Future studies could investigate the use of meta-learning to address intelligent algorithm challenges in
energy management.
Future Research
• Future research could address the duration intelligent algorithm agents need to relearn new policies
• Comparative studies of different algorithms and control methods could be valuable
• Merging multiple intelligent systems for energy control is a potential research direction.
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5. Conclusions and future directions in field
In conclusion, the increasing demand for sustainability and
concerns about energy exhaustion have made energy
management a significant topic in this era of globalization and
technological advancement. This paper has explored the
applications of artificial intelligence (AI) in energy management,
specifically focusing on areas such as demand response, smart
grids, and energy forecasting. The use of intelligent algorithms
and artificial neural networks (ANNs) in energy management
systems has been discussed. The review emphasizes the
importance of AI models in predicting energy consumption,
load patterns, and resource planning to ensure consistent
performance and efficient resource utilization. The
implementation of AI in energy management has shown
promising results, with reported energy savings of over 25%.
However, it is important to acknowledge that training AI model
requires large volumes of data, necessitating the utilization of
big data systems and data mining techniques to identify new
functions and associations that can enhance AI performance.
Additionally, the integration of advanced digital technologies
such as the Internet of Things and blockchain can further
enhance intelligent energy management. As a future scope of
this work, it is posited that the integration of multiple AI
techniques to generate hybrid models has the potential to
significantly improve prediction accuracy. The future
investigations should focus on deep learning models, long-term
prediction, component-based target variables, ensemble
models, lighting models, grey-box models, automated
architecture selection methods, and sliding window re-training.
These directions have the potential to improve energy
management models, enhance energy usage, contribute to data
science, and facilitate big data analysis.
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