Revolutionizing Energy Harvesting: Integrating AI
with Ambient Energy Sources
Gaydaa Al Zohbi
Department of Mechanical engineering-Prince Mohammad Bin Fahd University- Khobar- Saudi Arabia
Abstract: Addressing the critical need for sustainable power from intermittent ambient sources (thermal, kinetic, RF),
this paper presents a comprehensive review of Artificial Intelligence (AI) integration in energy harvesting. We
synthesize advancements in AI-driven design optimization, predictive forecasting, and real-time control,
demonstrating profound enhancements in efficiency, energy output, and system lifespan. Crucially, this review
provides an incisive analysis of fundamental challenges—from data quality to computational demands—offering
actionable guidance for developing sustainable AI-powered energy capture methods and accelerating global
decarbonization
Keywords: Energy Harvesting, Ambient Sources, kinetic energy, radiofrequency, Artificial Intelligence
1- Introduction
As the global energy demand continues to rise due to the depletion of natural sources, and population growth, there is
an increasing need for alternative energy harvesting methods. Climate change, caused by burning oil and gas for
energy production, poses a challenge by contributing to global warming and increasing the demand for cleaner and
more resilient energy production methods. The limited nature of oil and gas makes urbanization another challenge that
increases the demand for energy harvesting. Energy harvesting is capturing energy from external sources, with ambient
sources being one of the key alternatives. Ambient energy sources can be described as the process where energy is
obtained from the environment, including untapped ambient energy in our surroundings — such as heat, motion, and
radio waves — that remains largely unused and offers excellent potential for clean energy production [1]. The main
types of ambient energy discussed in this study are thermal energy, kinetic energy, and radio frequency (RF) energy.
Thermal energy represents the energy that can be extracted from natural or artificial heat sources. Kinetic energy
converts mechanical vibrations or human motion into electrical energy. Radio frequency is a form of ambient
electromagnetic radiation that can be converted into electrical power using induction with dedicated devices called
RF harvesters. These ambient energy sources are promising. However, capturing them can be challenging due to their
inconsistent availability and low energy density, as these sources depend on environmental conditions and suggest
small-scale energy production. Overcoming these challenges requires innovative solutions, one of which is the
integration of artificial intelligence (AI).
AI systems are essential for performing tasks similar to human intelligence (understanding, analysis, and decisionmaking). They operate through artificial means like software algorithms, machines, or computer systems rather than
biological processes. Integrating AI in this study is important because it can help in machine learning techniques,
supervised and unsupervised learning applications, deep learning approaches, neural networks for pattern recognition,
and reinforcement learning adaptive optimization of harvesting systems. The integration of AI with ambient energy
sources improves energy capture efficiency, enables predictive maintenance to reduce costs, supports smart grid
management, and enhances system adaptability to energy fluctuations. The integration of AI into the broader energy
sector is not a recent trend, with its foundations established in the 1990s. Early implementations of AI initially
concentrated on critical domains, including predictive maintenance and resource Management. This early integration
intended to improve system security, enhance dependability, and enable preventive maintenance of energy
infrastructure, establishing a vital foundation for the broader and more sophisticated deployments observed today.
This historical foundational was pivotal in revealing the AI's capabilities in optimizing complicated operational tasks
and reducing risks in the energy sector.
The market of energy harvesting system is experiencing a significant development, with predictions reflecting a
notable enlargement. The market size is predicted to attain USD 0.94 billion by 2030, jumping from USD 0.61 billion
in 2025, revealing a Compound Annual Growth Rate (CAGR) of 9.1% during this predicted timeframe [2]. This strong
upward trend is largely driven by the growing integration of AI, that consistently boosts system efficiency, advances
predictive analytics functions, and maximizes overall system efficiency within the energy harvesting area. The current
fast-paced growth of the energy harvesting market goes beyond mere coincidence with the presence of AI; it is directly
boosted by the expanding maturity and widespread use of AI potential. The swift from AI's historical contribution in
predictive maintenance to its extensive deployment throughout broad system optimization and smart energy
management highlights its role as an essential market growth factor and an effective market differentiator. Recent
innovations in machine learning, combined with the rapid growth of Internet of Things device and a considered
enhancement in data collection techniques, have jointly increased AI's applications and effects [3]. For instance, the
large-scale adoption of IoT sensors, provides a huge amount of real-time functional and environmental data, which
provides the necessary momentum for advanced AI algorithms to go beyond individual applications. It leads to have
a far-reaching, market-transforming effect, enhancing development pushing the industry onward.
The rising global demand for sustainable energy, along with the intermittent and location-dependent nature of
ambient sources (such as thermal, kinetic, and RF), calls for adaptive and systematic power generation strategies.
Although the integration of Artificial Intelligence (AI) into these systems is widely acknowledged for its
transformative potential, the current literature lacks a thorough and critical review of its applications, performance
improvements, and challenges across all major ambient energy harvesting domains. This paper addresses this critical
gap by presenting a comprehensive, state-of-the-art review that systematically explores how AI models are reshaping
the design and optimization of energy harvesters. It examines AI's role in improving energy prediction, enabling realtime functional recalibration, and enhancing system reliability through predictive maintenance. The review
underscores significant gains in efficiency, energy output, and operational lifespan achieved through the integration
of AI. Additionally, the paper directly addresses key challenges—such as data quality, computational demands, and
integration complexities—providing valuable guidance for developing more efficient and sustainable AI-driven
energy harvesting solutions and supporting the global transition toward a decarbonized future.
2- Overview of Ambient Energy Sources
2.1 Thermal Energy Harvesting (TEH) principles and technologies
Thermal energy, produced by the random motion of atoms and molecules, is abundant in the environment and can be
harvested from sources such as solar radiation, heated air, or warm surfaces. Globally, a huge amount of primary
energy consumption is dispersed as waste heat, making TEH an essential element in enhancing the total energy
efficiency, leading to a reduced greenhouse gas emission, and a promoting sustainability. This technology holds great
promise for powering low-power electronics, IoT devices, wireless sensor networks, also aiding to large-scale energy
recovery from industrial operations and vehicle emissions. It serves as a valuable power source for low-power
electronics through three main energy conversion principles: thermoelectric, pyroelectric, and thermophotovoltaic
effects [4].
Thermoelectric harvesting relies on the Seeback effect, where a temperature difference across a material generates
voltage. Devices such as thermoelectric generators (TEGs) use semiconductor materials to convert heat into electricity.
When one side of a thermoelectric material (or a junction of two dissimilar materials) is heated and the other is cooled,
mobile charge carriers (electrons or holes) at the hot end preferentially diffuse towards the cold end. Thermoelectric
generators (TEGs) devices are crafted to capture the Seebeck effect. They are composed of p-type and n-type
semiconductor materials (thermoelements) linked electrically in series and thermally in parallel. The flow of electron
from hot to cold in the n-type materials and holes from the cold in the p-type materials is driven by a temperature
different between the hot and the cold side of the TEG, resulting in producing DC voltage. Common thermoelectric
materials involve Bismuth Telluride (Bi₂Te₃) for low-to-mid temperatures (e.g., body heat, industrial waste heat <
200°C), lead telluride (PbTe) for mid-range temperatures, and silicon germanium (SiGe) for high-temperature
applications (e.g., aerospace, industrial furnaces) [5]. Recently, research has been done on exploring the purified tin
selenide to enhance the efficiency since it offers some advantages in terms of no moving parts, extremely reliable,
quiet functioning, long lifetime, and adaptable for different power outputs [6]. The main applications of TEGS are
wearable devices in human body heat, waste heat recovery from industrial process, remote power for sensors, and
automotive exhaust. A high Seebeck coefficient is needed to produce a higher voltage for a provided temperature
different. Maintaining an electrical resistance and reducing power loss requires a high electrical conductivity.
Additionally, maintaining the temperature difference across the materials and reducing heat loss demands a low
thermal conductivity. While TEGs are reliable and compact, they suffer from a low efficiency, especially when the
temperature difference is small, and are highly dependent on maintaining a hot–cold temperature gradient [4].
Pyroelectric generators produce electricity in response to time-varying temperatures. These devices are useful in
environments with fluctuating heat but are limited by low energy output and less widespread application compared to
TEGs. The fluctuations in the material's overall temperature change its internal atomic structures, resulting in a net
electrical charge on its surfaces. This charge could generate a current when an electric circuit is connected. The current
produced by pyroelectric devices is alternating current (AC) as they respond to temperature variations. The main
materials used in pyroelectric are Lithium Tantalate (LiTaO₃), Lead Zirconate Titanate (PZT), and various polymers
[7]. The main advantages derive from their ability to function effectively with minor temperature variations, with a
possibility for miniaturization. However, they encounter some challenges related to the need of dynamic temperature
changes, and comparatively reduced power output compared to TEGs for steady heat sources. They could be used in
specialized applications where temperature fluctuations are common.
Thermophotovoltaic (TPV) systems convert infrared radiation emitted by hot surfaces into electricity using
photovoltaic cells [8]. A hot emitter radiates photons, which are then absorbed by a low bandgap PV cell. The energy
from these photons excites electrons in the PV cell, driving an electric current. TPV systems are especially appropriate
for high-temperature heat sources. The common materials used in TPV are III-V semiconductors (e.g., GaSb, InGaAs),
specially engineered with bandgaps to absorb infrared radiation [9]. TPV systems offer some advantages, by providing
high power density under high temperatures, with a possibility of enhanced efficiency with spectrally improved
emitters and filters. They could be used for high temperature waste heat recovery, potentially for concentrated solar
power. TPVs offer the potential for high efficiency but require high operating temperatures and face complex material
and system design constraints. Across all thermal energy harvesting technologies, material degradation presents a
significant challenge, as prolonged exposure to heat and harsh environmental conditions can reduce device
performance, shorten operational life, and increase maintenance costs [10]. These limitations restrict the scalability
and reliability of thermal harvesters in real-world applications.
2.2 Kinetic Energy Harvesting (KEH) mechanisms and applications
Kinetic energy, the energy of motion, is widely presented in the form of mechanical vibrations from buildings,
machines, and human activity, making it a valuable source for powering low-energy devices [11]. KEH offers a
promising route to achieving a sustainable and self-sustaining power sources for a diverse range of low-power
electronic devices, such as wearable electronics, wireless sensor networks (WSNs), remote monitoring systems, and
Internet of Things (IoT) devices. KEH reduce maintenance expenses, prolongs the lifespan of devices, and reduces
environmental issues linked to battery disposal owing to the reduction or elimination of using conventional batteries.
A transduction mechanism is needed to convert mechanical motion into electrical energy, and a mechanical system to
effectively link environmental displacements to this mechanism. Several methods are used to convert this motion into
electricity, including piezoelectric, electromagnetic, electrostatic, and hybrid techniques.
Piezoelectric materials produce a voltage when exposed to a mechanical stress or stain. Alternatively, they distort
when an electric field is applied. The working principle is based on deforming the piezoelectric materials by the
mechanical energy applied (such as vibrations, pressure), leading to a displacement of charge centers inside its crystal
lattice. This induces a voltage across the material's surfaces. Piezoelectric harvesters commonly use cantilever beams
with a mass for measuring proof, where vibrations induce oscillations in the beam, emphasizing the piezoelectric
component. Design simplicity and possibility for miniaturization, as well as the potential of producing significantly
high voltage with a high energy density are the main benefits of using piezoelectric. The drawbacks of piezoelectric
materials are their brittle, limiting their mechanical robustness and incorporation into flexibles structures [12].
Moreover, they need an efficient mechanical connection to the movement source, and they generate lower power
compared to electromagnetic for some application, particularly at low frequencies.
Electromagnetic harvesters, such as spring–mass systems, micro-electro-mechanical systems (MEMS) magnet–
coil setups, and commercial modules, perform well at low frequencies but suffer from bulky size, miniaturization
difficulties, and integration complexity. The working principle of electromagnetic energy harvesting is based on
electromagnetic induction, which indicates that a motion of a conductor relative to a magnetic field or a change of a
magnetic flux leads to induce a voltage across a conductor. Electromagnetic energy harvesting system include a coil
of wire with a permanent magnet and it induces an electromotive force by creating a relative movement between the
coil and the magnet. This system is comparatively sturdy and long-lasting, and could generate higher power,
particularly with greater displacements or more intense magnetic fields. In addition, its output impedance is lower,
which can be simpler to handle with power electronics. However, it requires a bigger size compared to electrostatic
devices for a similar output power.
Electrostatic devices include MEMS capacitors and electret-based systems. The operational principle of
electrostatic energy harvesting is founded on the idea of a varying capacitor with the mechanical motion to convert
the mechanical energy into electrical energy. The electrostatic system includes moving electrodes, commonly
arranged as part of a parallel-plate capacitor design. The capacitance changes with the application of external
mechanical force that alters the distance between the electrodes or their overlapping surface area. Pre-charging the
capacitor results in enhancing voltage and producing electricity. This system is well adapted for MEMS because of
manufacturing computability and can attain high power densities under some arrangements [13]. However, this system
demands an initial voltage for an effective operation and is vulnerable to some environmental factors.
Hybrid energy harvesting system is considered as a viable solution to address the intermitted and unpredictable
issue of kinetic energy. It combines kinetic energy and other ambient energy sources and is increasingly used. For
instance, solar-vibration hybrid harvester system incorporates a small solar panel in a piezoelectric or electromagnetic
harvest to generate electricity by capture sunlight and ambient vibrations. This is mainly beneficial for device installed
outdoor that encounter both mechanical motion and sunlight. Thermal-RF hybrid harvester system is another example
that combine a thermoelectric generator with a radio frequency energy harvester. This hybrid system is able to energize
devices in settings with waste heat and ambient RF signals, such as Wi-Fi. This could offer a more dependable and
consistent power supply, combining the benefits of different sources. Hybrid system typically includes a single,
advanced power management with the ability to manage energy from different input sources, enhancing the charging
of the storage unit, and effectively supplying power to the load. However, hybrid and nonlinear system add design
complexity, lower energy density, and power overheads at small scales.
While each method shows potential, they all face key limitations. The integration of artificial intelligence (AI)
offers a promising path to overcome these challenges by improving efficiency, adaptability, and control. Despite the
fact that kinetic energy harvesting devices offer key advantages—such as maintenance-free operation, long lifespans,
and environmental sustainability—they also face specific limitations depending on the method used. These include
narrow operating frequency ranges in piezoelectric devices, miniaturization challenges and bulkiness in
electromagnetic systems, low output power and pre-charging requirements in electrostatic harvesters, and increased
design complexity with lower energy density in hybrid configurations. If unaddressed, these drawbacks can
significantly limit their effectiveness in real-world applications. The integration of artificial intelligence (AI) presents
a promising pathway to overcome these challenges by enabling real-time optimization, adaptive control, and enhanced
system efficiency.
2.3 Radio Frequency energy harvesting technologies and potential uses
Radio Frequency (RF) energy harvesting is the process of capturing and converting electromagnetic energy from radio
waves into usable electrical power. This technology presents a viable solution for energizing low-power devices
electronic devices, minimizing the dependence on conventional batteries and facilitating new applications across
different sectors [14]. Different sources emit RF energy, involving radio and television broadcast tower, mobile phone
base stations, Wi-Fi routers, and specialized RF transmitters. RF energy harvesting usually requires an antenna to
receive the electromagnetic waves, followed by a rectification circuit to convert the AC signal into DC power. The
harvested DC power can subsequently be used to directly supply a device or stored in a capacitor or battery for
subsequent use. The Friis transmission equation determine the quantity of harvest power received by an antenna at a
specific distance from a transmitting antenna [15]:
Friis transmission equation 𝑃𝑟 =
𝑃𝑡𝐺𝑡𝐺𝑟𝜆
2
(4𝜋𝑅)2
(1)
where Pr is the power at the receiving antenna, Pt is the output power of transmitting antenna, Gt is the gain of the
transmission antenna, Gr is the gain of the receiving antenna, is the wavelength, and R is the distance between the
antennas
The design and characteristics of the antenna, including shape, size, and impedance marching, considerably affect
the effectiveness of the captured energy [16]. Dipole antennas, microstrip antennas, and dipole antennas are the most
common antenna types. The rectification circuit consists of diodes, and it is used to convert the AC signal received on
to DC power. Schottky diodes are often favored because of their low forward voltage drop and rapid switching speed,
which are crucial for efficient RF energy conversion. The maximum transferred power between the antenna and the
rectification circuit is achieved when their impedances are aligned. An impedance matching network is commonly
employed to ensure that the antenna's impedance is conjugately matched to the input impedance of the rectifier,
reducing power loss and maximizing energy transfer. The power management circuit is used to modulates and adjusts
the harvested DC power to align with the needs of the electronic device being powered. It can include voltage
regulators, DC-DC converters, and energy storage elements, like rechargeable batteries or capacitors.
RF energy harvesting could deliver a continuous power supply for wireless sensor networks (WSNs), removing the
necessity for frequent battery changes [14]. This is especially advantageous for applications in remote or hard-to-reach
locations, like industrial automation, and environmental monitoring. Additionally, RF energy harvesting could be used
to power a broad spectrum of IoT devices, such as smart home devices, and wearable sensors. This can facilitate the
creation of battery-free IoT devices, minimizing maintenance cost and broadening their potential applications.
Moreover, RF energy harvesting could be incorporated into wearable devices, like fitness trackers, smartwatches, and
medical sensors, to ensure a continuous power supply. This can boost the convenience and functionality of these
devices, eliminating the need for frequent charging. In medical devices, RF energy harvesting could be used to power
implantable medical devices, like drug delivery systems, and pacemakers. Moreover, RF energy harvesting is already
used in passive RFID and NFC tags, where the energy from the reader's RF signal is used to power the tag's circuitry.
3- AI Methodologies in Energy Harvesting
The efficiency of harvesting energy from ambient sources depends on several factors, including environmental
conditions, system parameters, and load characteristics. Artificial Intelligence (AI) methodologies are increasingly
utilized to tackle these challenges and improve the efficiency of energy harvesting technologies [17]. The AI
techniques, including machine learning, deep learning, and optimization algorithms, are being applied in various areas
of energy harvesting.
3.1 Machine Learning
Machine learning algorithms serve as the structural foundation for AI applications in energy harvesting. Algorithms,
such as linear regression, neural networks, and support vectors machine could be trained on labeled data to forecast
energy output, optimize system settings, and categorize system states. Additionally, clustering algorithms, such as kmeans clustering could be used to detect patterns in energy source data and enhance energy harvesting strategies.
There are different types of machine learning, including supervised, unsupervised, and reinforcement learning.
- Supervised learning: This approach entails training models on supervised datasets where each data
point is associated with an intended outcome. The model trains to associate inputs with corresponding
outputs, allowing it to forecast outcome for new, unfamiliar input data. Supervised learning is
frequently used to predict energy demand and supply in energy harvesting, analyzing previous data
patterns to project future behavior. Some applications involve regression models for predicting
continuous values such as power outputs, and categorization models for classifying systems states or
detecting possible failures.
- Unsupervised learning: unsupervised learning handles data without predefined labels, aiming to
uncover underlying patterns, structures, or relationships within the data set in the absence of known
output values. Methods like clustering are employed to categorize similar data, which is useful for
dividing energy consumption patterns into meaningful segments or recognizing different functional
modes of energy harvesters. Dimensionality reduction methods assist in reducing the complexity of
large datasets, streamlining them for integration with analysis and visualization in energy applications.
- Reinforcement Learning (RL): RL embodies an advanced framework where an intelligent agent learns
to optimize decisions by repeatedly interacting with the environment. The agent performs actions and
receives feedback in the form of rewards or penalties, continuously adjusting its approach to enhance
cumulative gains over time through experiential learning. This learning strategy is ideally suited for
adaptive and evolving energy environments, like maximizing energy storage and distribution with
smart grids. Well established algorithms involve Q-learning, which tracks the expected value of each
state-action pair in a table format, and policy gradient techniques, that explicitly enhance the agent's
decision-making policy. The flexibility and real-time learning features of RL are increasingly
acknowledged for their dynamic response capabilities to the dynamic demands and natural
uncertainties associated with modern energy systems.
The transition toward reinforcement learning techniques denotes an important shift away from purely predictive AI to
systems cable of autonomous, adaptive control in energy applications. Renewable energy sources naturally display
fluctuating and intermittent behavior creating persistent obstacles for grid stability and efficient resource utilization,
despite the forecasting and pattern recognition of supervised and unsupervised learning models. RL's capability to
acquire knowledge from real-time interactions and modify behavior accordingly mitigates this foundational concern.
This allows RL-driven systems to evolve from passive prediction to execute efficient control strategies with the ability
to adapt instantly to changing energy landscapes. This ability is essential for the efficient management of the
fluctuating nature of renewable energy, transitioning from static models toward dynamic, self-optimizing energy
systems.
3.2 Deep Learning Architectures
Deep learning (DL) constitutes a more sophisticated subset of machine learning defined by multi-layer neural network
architectures that can manage extensive and highly detailed datasets. This architectural depth enables DL models to
derive hierarchical patterns from raw datasets without manual intervention, resulting in a considered improvement
precisions in tasks, including predictive modeling of energy loads and detection of irregular patterns. DL models are
widely recognized for their high-speed processing of relevant data features, strong ability to generalize across diverse
scenarios, and intrinsic strength in handling vast, complex data, enabling their strong performance in a wide range of
energy related fields. Some of DL architectures are:
- Convolutional Neural Networks (CNNs): This tailored DL architectures are highly effective in
handling grid-like data, like images or time-series data with spatial correlations. CNNs are well-suited
for identifying and interpreting spatial structures in energy harvester designs or for detecting minor
imperfections in solar panels from visual or thermal imaging data. They are perfect for automated
inspection and quality control in large energy generation facilities.
-
- Recurrent Neutral Networks (RNNs) and Long Short-Term Memory (LSTMs): RNNs and their more
sophisticated variant, LSTMs, are crafted to Operate on time-series inputs, and are highly effective for
analyzing temporal patterns. This renders them extremely valuable for modeling and forecasting the
effectiveness of energy harvester during time, modeling sophisticated temporal dynamics and
interdependencies over historical sequences for more precise future prediction. The vanishing gradient
challenge typical of classic RNNs is mainly addressed by using LSTMs, enabling them to learn deep
temporal dependencies across extended sequences.
- Deep Neural Networks (DNNs): DNNs, are neural networks with multiple hidden layers, designed to
be trained with large volumes of data to determine the best design parameters for different energy
harvesting systems. In piezoelectric vibration energy harvesters, DNNs have been effectively utilized
to optimize the electrical and mechanical parameters to enhance power output.
- Generative Adversarial Networks (GANs): GANs involve two neural networks, generator and a
discriminator, that engage in adversarial training to generate synthetic data that appears the same as
real data. GANs are used to simulate various patterns of energy use in energy optimization, facilitating
long-term planning and strategy development, risk assessment, and identifying anomalies through the
generation of realistic synthetic data for model training and validation. Additionally, GANs has also
demonstrated potential in designing optimal topologies for energy system design, like the results
derived from level set topology optimization.
3.3. Evolutionary Algorithms and Hybrid Approaches
Evolutionary algorithms and hybrid AI approaches provide robust solutions for maximizing complex energy
harvesting systems, particularly effective in handling multi-objective problems or extensive design domains.
Evolutionary algorithms and hybrid AI approaches include genetic algorithm (Gas) and hybrid approaches.
- Genetic Algorithms (GAs): GAs are optimization algorithms, based on the principles of nature, that
gradually refine a set of potential solutions through repeated iterations. Possible solutions are modeled
as "chromosomes," subjected to genetic operators like crossover and mutation to create new cohorts
of candidate solutions. The "fitness" of each solution is assessed using an objective function tied to
energy harvesting efficiency or other relevant measurement parameters. GAs demonstrates strong
capability in exploring novel energy harvester designs or configurations. Their effectiveness stems
from their ability to optimize multiple parameters simultaneously and discover optimal solutions on a
global scale, even in complex, non-linear problems. A reinforcement learning-enhanced genetic
algorithm (RLGA) has achieved roughly three times higher efficiency compared to traditional Gas in
wind farm layout optimization, mainly for complicated configurations. This enhancement is due to
RL's real-time adjustment of key parameters throughout the evolutionary cycle throughout the GA
process, reducing the risk of getting trapped in local optima and accelerates convergence.
- Hybrid approaches: these approaches bring together diverse AI techniques or incorporate AI with
conventional modelling and control strategies to capitalize on the complementary advantages of each
component. For example, hybrid models, that combine deep learning with traditional Artificial Neural
Network (ANN)-based Maximum Power Point Tracking (MPPT) controllers, have been emerged to
boost energy extraction and power quality in solar photovoltaic systems. In wind control, deep learning
models integrated with reinforcement learning to enable a more active and adaptive power strategies,
managing generator and blade pitch control systems according to aerodynamic predictions. These
hybrid approaches seek enhanced performance through overcoming the limitations of individual
techniques, resulting in more resilient, accurate, and optimized energy harvesting and management
systems.
Table 1 outlines the different role of AI in enhancing, along with some of their key applications in the
different types of energy harvesting. It summarizes the contribution of AI in material science, system design,
performance optimization, and predictive maintenance across thermal, kinetic and radio frequency energy
harvesting techniques.
Table 1 . Artificial Intelligence in energy harvesting applications [17-21]
Energy
harvesting type
Role of AI Applications
Thermal energy - Materials Discovery and Optimization
- System Design and Optimization
- Energy Prediction and Forecasting
- Industrial Waste Heat Recovery
- Automotive TEG Systems
- Building Energy Management
- Adaptive Control and Optimization
- Fault Detection and Predictive
Maintenance
- Wearable Devices
Kinetic energy - Optimizing Energy Conversion
Efficiency
- Predictive Frequency Tuning
- AI-Enhanced Mechanical Design
- Fault Detection and Predictive
Maintenance
- Robust Design Optimization
- Material Optimization
- Improving System Integration and
Scalability
- Wireless sensors
- Wearable Electronics
- Autonomous Robotics
- Some emerging applications
(smart cities, ocean monitoring,
and space exploration)
RF - Optimized Energy Capture (Smart
Antenna Design and Adaptive
Impedance Matching)
- Enhanced Power Management
(Intelligent Rectifier Circuits and
Predictive Energy Management)
- Improved Localization and Source
Tracking
- Internet of Things (IoT)
- Wearable Devices
- Wireless Smart Home Devices
- Industrial Applications
- Implantable Medical Devices
Table 2 displays Fundamental AI approaches and their practical applications in energy harvesting
categorizing different AI approaches. it highlights the main application of each AI model in energy harvesting
and details its core features and advantages.
Table 2 Fundamental AI approaches and their practical applications in energy harvesting
AI Methodology Principal application in
Energy Harvesting
Core features/
Advantages
References
Machine Learning
(General)
Energy system
optimization, pattern
recognition, predictive
maintenance
Analyzes large, dynamic
datasets; learns from
data to make
predictions/decisions
[22]
Supervised Learning Energy demand/supply
forecasting, fault
classification
Utilizes labeled data to
predict outcomes;
regression and
classification models
[23]
Unsupervised
Learning
Identifying patterns in
energy data,
dimensionality reduction
Discovers hidden
structures in unlabeled
data; clustering
techniques
[23]
Reinforcement
Learning (RL)
Dynamic control,
adaptive harvesting
strategies, grid
balancing, wind turbine
control, wind farm
layout optimization
Learns optimal actions
through interaction with
environment; adaptive
decision-making;
maximizes cumulative
rewards; addresses
intermittency
[24]
Deep Learning (DL) Advanced forecasting,
anomaly detection,
material discovery,
design optimization
Multi-layer neural
networks process
complex data; rapid
feature extraction; robust
generalization
[25]
Convolutional Neural Spatial pattern analysis, Specializes in grid-like [26]
Networks (CNNs) solar panel defect
detection, image
analysis for maintenance
data; extracts feature
from visual/thermal
imagery
Recurrent Neural
Networks (RNNs) &
Long Short-Term
Memory (LSTMs)
Time-series analysis,
energy performance
forecasting
Effectively handles
sequential data; captures
temporal correlations for
predictions
[26]
Deep Neural Networks
(DNNs)
Optimizing design
parameters for
harvesters
Trained on large datasets
to find optimal
mechanical/electrical
parameters
[27]
Generative
Adversarial Networks
(GANs)
Simulating energy
scenarios, optimal
topology design
Generates synthetic data;
supports strategic
planning and anomaly
detection; produces
optimal designs
[25]
Genetic Algorithms
(GAs)
Novel design discovery,
multi-parameter
optimization, wind farm
layout optimization
Simulates natural
selection; explores large
solution spaces; finds
global optima
[24]
Hybrid Approaches Enhanced MPPT for
solar PV, wind turbine
control, improved
forecasting
Combines strengths of
different AI/modeling
techniques for superior
performance
[26]
3- Recent Studies and Experimental Applications of AI in Energy Harvesting
3.1. Solar Photovoltaics (PV)
3.1.1. AI for Solar Irradiance forecasting and power output prediction
Reliable near-term prediction of photovoltaic (PV) power generation is critical for sustaining grid stability,
optimizing resource allocation, and enabling smooth integration of solar energy into national grids. Historical
weather patterns, real time environmental data, and solar radiation levels are used by AI powered forecasting
tools to forecast future solar power generation significantly enhanced predictive accuracy over traditional
techniques. The effectiveness of these AI tools is demonstrated in several recent studies. A study conducted
by [28] shows an enhancement of solar forecasting rates with a prediction error margin of just 2–3%, the
model significantly outperforms conventional forecasting approaches, which commonly exhibit variances of
8–10% from actual generation values. A novel deep learning architecture, known as the Transformer-Infused
Recurrent Neural Network (TIR) model has shown outstanding accuracy in forecasting solar irradiance,
achieving R2
of 0.9983, a Root Mean Square Error (RMSE) of 0.0140, and a Mean Absolute Error (MAE)
of 0.0092 [29, 30]. These results highlight the model's superior accuracy relative to other approaches
assessed. Another study demonstrated the CatBoost model emerging as a top performer in solar energy
forecasting, using different input parameters, including ambient temperature, humidity, visibility,
atmospheric pressure, wind speed, and cloud ceiling to improve its forecasting accuracy [31]. Additionally,
deep learning algorithms, like the time-series dense encoder (TiDE), have shown excellent performance in
short-term prediction tasks, with an R2
of 0.952 for 5-minute-ahead predictions [32]. This model also
demonstrated an enhanced accuracy and adaptability in adverse weather scenarios including cloudy and rainy
environments. AI allowed grid operators to facilitate greater incorporation of solar energy into the grid while
ensuring higher operational confidence, by giving enhanced precision and reliability in forecasting. The need
for conventional, less flexible backup power sources, which are usually fossil fuel based is reduced owing to
this improved predictability. This is crucial to support the attainment of strategic decarbonization objectives
and rising the total proportion share of renewable energy in the total energy mix.
3.1.2. AI in solar panel positioning, design, and performance optimization
AI systems play a fundamental role in the installation and operational effectiveness of solar model using
advanced optimization of positioning, module design, instantaneous power control configuration. A
continuous processing and analysis of diverse environmental factors, such as cloud cover, humidity,
precipitation, and temperature. This adaptive optimization feature can result in a significant enhancement in
the total energy generation, with research indicating enhancements of 10-15% compared to traditional fixedtilt solar installations [33]. Additionally, layout optimization algorithms reduce the time needed to design of
solar projects as well enhancing energy generation by 3-8%, owing to its ability to assess thousands of panel
configurations within minutes [34]. In term of shade analysis, AI-based shade analysis reaches an outstanding
accuracy of 98%, significantly reducing the time and minimizing potential errors related to manual site
evaluation [35]. This accuracy assures that panels are optimally to optimize sunlight exposure and reduce
shading losses. Hybrid AI solar dual-axis tracking system combines Convolutional Neural Networks (CNNs),
Long Short-Term Memory (LSTMs), and reinforcement learning achieved a significant 41.1% improvement
in yearly energy generation, an 18.7% enhancement in spectral absorption efficiency, and an average decrease
of 11.9°C in panel temperature when compared to conventional fixed-tilt systems [36]. A remarkable solar
tracking efficiency of 95% is achieved using the system, with reinforcement learning driving additional gains,
tracking precision improves to 98.3% [37]. This smart energy management achieve to a 60% enhancement
in battery lifetime, emphasizing the comprehensive advantages of AI implementation.
3.1.3. AI-Driven predictive maintenance for solar farms
AI is redefining maintenance strategies in large scale through transitioning grom reactive repairs to proactive,
predictive interventions. A large quantity of operation data is analyzed to proactively identify and mitigate
potential system failures. Sophisticated machine learning algorithms consistently track key performance
metrics in real time, such as thermal pattern, voltage inconsistencies, and power output variations, to detect
any early indicators of wear and potential component failure. The use of drone-based image analysis
integrated with AI powered recognition, outfitted with infrared and standard visual sensors to promptly assess
expansive solar farms, detecting issues like micro-cracks, dirty panels, and shading problem. Then AI
algorithms are used to analyze imagery instantly on site, document and map issues for technical intervention.
This automated and accurate early fault detection enables considerably accelerated repair times and
minimized system downtime. The integration of these AI-driven predictive maintenance systems has
delivered significant advantages. Some studies pointed out a reduction to a 30% of the total maintenance cost
and a 25% enhancement in system availability for solar installations [38]. Early and effective fault
identification has also showed a remarkable enhancement, achieving up to 95% [39]. These improvements
promote increased reliability alongside cost savings in solar power production systems, maintaining reliable
functionality throughout its full-service life.
3.2. Wind Energy Systems
3.2.1. AI-Enhanced Wind Power Forecasting and Grid Integration
The accuracy prediction of wind power production is an essential component for sustaining operational
stability in electrical grids and streamlining the incorporation of fluctuating wind energy into power systems.
The predictions are considerable enhanced by using AI models through analyzing large-scale datasets that
involve wind speed, wind direction, air pressure, and temperature, supporting the detection of complex trends
that impact energy generation. The application of Explainable AI (XAI) is revolutionizing this field by
improve the transparency and reliability of wind power predictions though delivering a deeper understanding
of the decision -making processes of black box AI models and determining the most impactful variables
driving model predictions [40, 41]. This clarity is essential for grid operators, who demand accurate forecasts
with minimal error margins, and a comprehensive insight into the fundamental processes to confidently
integrate wind power. XAI directly targets the underlying lack of trust faced by the tradition AI models, that
could Impede their widespread implementation in critical infrastructure, enhancing the acceptability of AIdriven forecast in grid integration. A superior accuracy has been achieved using hybrid forecasting models,
that combine the advantages of diverse machine learning algorithms. For example, a hybrid model combining
Support Vector Machine (SVM), Artificial Neutral Networks (ANN), and K-Nearest Neighbors (K-NN)
attained a Normalized Mean Absolute Error (NMAE) of 5.54%, Which is significantly below the NMAE
range of 5.65%–9.22% recorded in other examined models and considerably outperforming the average
NMAE reported in the literature [42, 43]. These improvements in predictive accuracy are essential for
optimizing the balance between energy demand and supply, improving the stability of the grid.
3.2.2. AI for wind turbine control and site layout optimization
AI is Allows for adaptive and accurate regulation of individual wind turbines and the comprehensive
optimization of entire wind farm layouts, resulting in improved energy harvesting and operational efficiency.
AI algorithms streamline the real-time optimization of turbine settings, including blade pitch and yaw angle,
in active response to variable weather conditions. The dynamic, real-time optimization processes can enhance
the energy generation of wind turbines by up to 20% [44]. Deep reinforcement learning (DRL) models are
being developed for the strategic and adaptive scheduling of advanced wind power systems. For instance, the
WindOpt-DQN model, based on DRL has achieved a 15% increase in cumulative reward and a 10% higher
generation efficiency than that achieved by traditional scheduling models, enhancing long-term decisions for
wind power systems [45]. These models acquire the ability to optimize and schedule effectively amid
changing system conditions and grid load conditions, evolving strategies via ongoing interaction with the
environment. In the field of enhancing the macroscopic efficiency of wind farms, reinforcement learningenhanced genetic algorithms (RLGA) are currently employed in the wind farm layout optimization (WFLO)
[46]. A strategic position of wind turbine using WFLO to optimize energy output and reducing the adverse
impacts of turbine wake interactions. RLGA has been shown to be roughly three times more effective than
conventional Genetic Algorithms (Gas), especially relevant for densely arranged or irregular layouts. This
improved efficiency is a result of ability of RL's to adaptively determine optimal parameters across the GA
process, systematically narrowing the search toward optimal or near-optimal outcomes, and improve
convergence efficiency in identifying superior layouts. Additionally, AI aids in streamlining the fundamental
design parameters of wind turbine, mainly in the design of aerodynamically high-efficient blade shapes
through fast-paced, iterative design refinement [47].
3.2.3. AI-based predictive maintenance for wind turbines
AI driven maintenance is enhancing system durability and long-term performance of wind turbine by
transitioning from routine or fault-driven maintenance approaches to predictive interventions. This method
leads to a substantial decrease in system downtime, enhance the overall reliability of the systems, and
significantly prolongs the turbine's service life [48]. AI models are designed to assess degradation in aging
turbines and precisely forecasting the end-of-life timeline for components, supplying vital insights for
informed decisions pertaining to refurbishment, upgrades, and decommission of the wind turbines. GE
Renewable Energy company is effectively using AI to monitor wind turbines, facilitating early detection of
mechanical failures and thus improving operational efficiency throughout their entire fleet of wind turbine.
This proactive identification of risks permits allows timely intervention, avoiding costly breakdowns, and
optimizing energy generation. In addition, AI facilitates positive environmental outcomes in wind energy.
for instance, AI application can aid in mitigating adverse effects on wildlife, including reducing bird
collisions with turbines, through enhancing operational efficiency of turbine or implementing preventive
measures based on real-time data and predictive models [49].
3.3. Thermal Energy Harvesting
3.3.1. AI in thermoelectric materials discovery and optimization
In thermoelectric, AI Is driving rapid advancements in discovery and optimization of new materials facilitates
efficient waste heat recovery for power generation, known as thermoelectric materials. This field presents
substantial opportunities to enhance the total energy efficiency of different systems. The conventional
processes for materials discovery involve considerable complexity, time investment, and resource
consumption, frequently requiring years of iterative testing and refinement. However, AI significantly
accelerating and simplifying the process. Machine learning models could systematically screen large-scale
simulated chemical datasets for new alloys, effectively filtering down to a focused set of high-potential
candidates and shortening the duration of the discovery process from years to few months [50]. The
development of a novel machine learning farmwork with an ability to forecast photon dispersion relations,
which is an essential property managing heat dispersion in materiel, up to 1,000 times quicker compared to
other AI models, and potentially outperforming conventional non-AI approaches by a factor of one million,
with achieving accuracy comparable to or surpassing traditional methods [10]. This led scientists to discover
a much bigger materials space while exploring options for materials with higher thermal storage capabilities.,
improved energy conversion properties, or even superconductivity. Additionally, machine learning
algorithms have been developed by researchers to accelerate and optimize the discovery and development of
high-performance of ionic thermoelectric materials, realizing a notable R² of 0.98 in forecasting Seebeck
coefficient for these materials [51]. The considerable contribution of AI on materials discovery for thermal
energy harvesting impacts various dimensions of the energy industry on a broad scale. Considering that up
to 72% of energy is lost in various conversion processes as waste heat, the capability to effectively convert
this heat into electricity via sophisticated thermoelectric materials marks a fundamental shift. AI actively
promotes the extensive deployment of waste heat recovery solution by enhancing the speed and costefficiency of high-performance thermoelectric material discovery [52]. AI in improving the comprehensive
efficiency of the energy landscape by mitigating a substantial and a widespread energy loss mechanism.
3.3.2. AI for waste heat recovery and thermal management systems
In addition to material discovery, AI is employed to improve system integration and operational control of
waste heat recovery and thermal management systems in different application. The acceleration of the
discovery process of thermoelectric materials can be efficiently embedded within current operational
frameworks, like solar panels, to harvest and convert their waste into supplementary electrical output,
resulting in enhancing the total system efficiency. Additionally, machine learning is being studied for its
potential in real-time forecasting within phase change materials (PCM)- based thermal management systems,
which are utilized for cooling electronic chips and packages [53]. Machine learning models could forecast
the time needed to reach a target melt-fraction level through analysis the transient spatial distribution of
surface temperatures. This forecasting functionality yields several key benefits, like minimized cost,
improved sustainable and enhanced reliability of cooling systems. Such strategies can contribute to the robust
stabilization of microprocessor temperatures and prolong the service life of electronic devices, notably those
subject to intermittent cycles.
3.4. Vibrational energy harvesting
3.4.1. AI for piezoelectric, electromagnetic, and electrostatic harvester optimization
AI, especially by employing a range of machine learning techniques, is revolutionizing the optimization of
vibration energy harvesters. These systems transduce ambient mechanical vibrations into usable electrical
energy, Providing an energy-efficient and sustainable power source for low-power devices and wireless
sensor networks. Some algorithms, including reinforcement learning, neutral networks and genetic
algorithms, are increasingly utilized to examine complex data patterns, accelerate the identification of
innovative design solutions, and allows real-time decisions intended to increase the effectiveness of energy
harvesting efficiency. In the case of piezoelectric vibration energy harvesters (PVEH), AI-based approaches
are fundamental for a multi-objective optimization [54]. This technique focuses on attaining a resonant
frequency while concurrently optimizing the power yield. Deep learning models, especially Deep Neural
Networks (DNNs), can be trained on large-scale datasets to determine optimal electrical and mechanical
factors for PVEH system [54], resulting in improving energy conversion efficiency. Additionally, machine
learning models, such as feed-forward neural networks and genetic algorithms, are being utilized to enhance
the synthesis of electro-spun polyvinylidene fluoride (PVDF)/polyurethane (PU) nanofibers [55]. This is
essential for enhancing their application in wearable nano-acoustic energy harvesters (NAEH), allowing them
to achieve high acoustoelectric power density.
3.4.2. AI in low-frequency and multi-mode vibration systems
Capturing vibrational energy from low-frequency and multi-mode vibration environmental, which frequently
experience low power generation in and limited operational bandwidths, in an efficient way is considered as
key limitations. AI is significantly contributing to overcoming these limitations. Machine learning models,
including gradient boosting regression trees (GBRT), decision tree regressors (DTR), and random forests
(RF), are used to reliably predict the performance of piezoelectric harvesters subjected to wake galloping
effects [56]. This forecasting ability facilitates an enhanced design and a control of systems to optimize
energy harvesting from complex, aeroelastic vibrations. Additionally, Dynamic vibrations Absorbers
(DVAs), which a crafted to mitigate vibrations, are continually enhanced using AI-based approaches. DVAs
prove enhanced vibration attenuation and expanded frequency response, concurrently enabling potential
advancements in energy capture [57]. Researchers are able to design DVAs, which serve not only to suppress
undesirable vibrations but also to harvest part of the mechanical energy as electrical output, fulfilling dual
functions in different application, ranging from industrial machinery to structural health monitoring.
3.5. Radio Frequency (RF) energy harvesting
3.5.1. AI for RF signal prediction and power management
Radio Frequency (RF) energy harvesting (RFEH) provides an effective strategy for continuously powering
low-power electronic devices, mainly in applications, where traditional batteries are not feasible or are
challenging to recharge, like biomedical body sensor. The presence of ambient RF energy exhibits
considerable variability depending on time, location, and spectrum, and may exhibit stochastic variations
influenced by environmental and operational factors. These variations create difficulties in maintaining a
consistent and an adequate power supply. Machine learning (ML) models are being implemented to mitigate
the effects of intermittency through predicting the optimal location for RF energy capture, resulting in
enhancing signal reception and maintaining dependable device functionality [58]. Several ML algorithms,
such as decision trees, linear regression, random forests, and support vector machines are used to simulate
optimal operational performance. Linear regression achieved the best performance in terms of accuracy and
stability in forecasting RF energy availability [59]. The capability to forecast periods of insufficient harvested
energy enable the system to undertake proper interventions, like activating wireless power transmission or
transitioning the load into a low-power sleep state, resulting in enhancing energy used and prolonging device
lifespan. The innovation of AI-driven adaptive harvesting systems with an ability for a dynamic adjusting to
varying RF environment will be the future of AI in RFEH.
3.5.2. AI in wireless power transfer optimization
AI play an essential tole in maximizing active wireless power transfer (WPT) systems. Deep learning
approaches are key contributors to improve the effectiveness and stability of these systems, mainly in
mitigating difficulties associated with frequency optimization and vulnerability to environmental fluctuations
[60]. The use of a gradient descent optimization algorithm, improved with topological properties to enhance
near field magnetic resonance WPT systems is a significant practical demonstration. This AI-optimized
model achieved superior results regarding both transfer efficiency and system robustness. This progress
exemplifies the way deep learning enhances the modeling of complex physical interactions to attain a superior
consistency and efficiency in energy delivery, unlocking advanced possibilities for powering devices in
difficult-to-reach or hostile settings, like electric vehicles and medical implants.
Table 3 highlights the substantial progress achieved through implementing AI intro different energy
harvesting methods. AI integration showed an ability to optimize strategic performance metric across energy
harvesting systems, including solar PV, wind power, thermal energy, and RF energy. the potent capability of
AI in different application in improving efficiency, precision, and reliability accord the spectrum of energy
harvesting, resulting in more effective and environmentally sustainable energy systems
Table 3 Performance Metrics of AI-Enhanced Energy Harvesting Systems
Energy Harvesting
System
AI Application Optimized Key
Performance
Indicator
Measurable
Enhancement
References
Forecasting (General) Forecasting Accuracy 2-3% error (vs. 8-10%
for conventional)
[61]
Forecasting (TIR
model)
R2, RMSE, MAE for
irradiance forecasting
R2=0.9983,
RMSE=0.0140,
MAE=0.0092
[29]
Forecasting (TiDE
model)
Short-term accuracy
(5-min ahead)
R2=0.952 [32]
Panel
Positioning/Yield
Overall Energy Yield 10-15% increase (vs.
fixed tilt)
[61]
Dual-axis Tracking
(Hybrid AI)
Annual Energy Yield 41.1% increase [62]
Dual-axis Tracking
(Hybrid AI)
Spectral Absorption
Efficiency
18.7% increase [62]
Dual-axis Tracking
(Hybrid AI)
Panel Temperature
Reduction
11.9°C average
reduction
[62]
Layout Optimization Energy Yield 3-8% higher (vs.
manual designs)
[63]
Shade Analysis Accuracy 98% accuracy [26]
Predictive
Maintenance
Maintenance Cost
Reduction
Up to 30% reduction [64]
Predictive
Maintenance
System Availability 25% improvement [64]
Solar PV
Predictive
Maintenance
Fault Detection
Accuracy
95% accuracy [61]
Turbine Control Energy Yield Up to 20% increase [65]
Forecasting (Hybrid
ML)
Normalized Mean
Absolute Error
(NMAE)
5.54% (vs. 6.73-
10.07% average)
[42]
Forecasting (XAI) Interpretability &
Trustworthiness
Improved insight into
model decisions
[66]
Scheduling (WindOptDQN)
Cumulative Reward 15% higher [45]
Scheduling (WindOptDQN)
Generation Efficiency 10% better [45]
Wind Energy
Layout Optimization
(RLGA)
Efficiency (vs. GA) ~3 times more
efficient
[46]
Material Discovery
(Phonons)
Prediction Speed Up to 1,000x faster
(vs. other AI); 1M x
faster (vs. non-AI)
[67]
Material Discovery
(Thermoelectric)
Discovery Time Reduced from years to
months
[68] Thermal Energy
Material Discovery
(Ionic Thermoelectric)
Seebeck Coefficient
Prediction
R²=0.98 accuracy [69]
Optimal Location
Estimation
Reception &
Reliability
Improved estimation
for sustainable
systems
[70]
RF Energy
Energy Modeling Accuracy & Stability
(Linear Regression)
Highest accuracy &
stability
[71]
4- Process Model of AI Integration in Energy Harvesting Systems
The use of AI digital tools transforms the traditional energy harvesting system into a smart, adaptive entity. The main
steps of a typical process are displayed in Figure 1 and explained below. Table 4 describes the successive and iterative
procedures of the AI integration lifecycle.
Table 4 Key Stages of the AI Integration Process Model with Associated Activities
Stage Main Tasks AI methods employed Goal/Result for energy
harvesting
Data collection and
preprocessing
Sensor deployment, Data
logging, Real-time data
streams,
Data cleaning, Missing value
imputation, Outlier detection
IoT, Edge Computing, Data
Streaming, Feature
Engineering, Statistical
Analysis, Data
Transformation
Real-time, granular data
from diverse sources;
Foundation for AI
Clean, consistent, and
relevant datasets; Improved
model readiness
Feature Engineering
selection/selection
Algorithm selection, Model
architecture design, Training
Performance metrics
(accuracy, reliability, costeffectiveness), Validation,
Overfitting analysis
Supervised/Unsupervised
ML, Deep Learning,
Reinforcement Learning,
Genetic Algorithms
Statistical Modeling, Crossvalidation, Benchmarking
Optimized models for
specific tasks (e.g.,
forecasting, optimization)
Validated model
performance; Assurance of
real-world utility
Model training and
Deployment
System integration,
Infrastructure setup,
Scalability planning
Cloud/Edge AI, API
integration, Containerization
Seamless operation within
existing energy infrastructure
Prediction,
actuation/control and
feedback loop
Real-time performance
monitoring, Anomaly
detection, Feedback loops
Predictive Analytics,
Anomaly Detection,
Continuous Learning, Digital
Twins
Continuous efficiency gains,
Proactive maintenance,
System resilience
Figure 1 AI driven workflow for smart energy harvesting systems
- Data collection and preprocessing: A continuous collection of real-time environment data (such as light
intensity, temperature, vibration frequency, RF signal strength), and a system functional data (like harvester
output voltage/current, battery state of charge, load demand) is performed by sensors. The collected data is
processed, standardized, and ready for AI model input.
- Feature Engineering/Selection: Pertinent attributes are identified or chosen from the raw data that are most
representative of energy supply, system efficiency, or possible issues.
- AI Model Training and Deployment: it includes offline training and online deployment/adaptation. In offline
training, historical and simulated data are used to train AI models (like Fuzzy logic, ML, RL, and NN) to
identify patterns, forecasting guidelines, or ideal control strategies. Then, the trained models are implemented
into microcontrollers or edge devices within the energy harvesting system. Some models, such as
reinforcement learning agents, can continuously learn and adapt in real-time through new interactions with
their environment.
- Prediction/Decision-Making: The real-input data is processes by AI model to forecast energy availability,
optimize operation, manage resources, and identify issues. AI models could predict the amount of energy
that could be harvested in the upcoming future. In addition, AI models can identify the optimal functioning
factors for the harvester and determine the allocation way of the captured energy between the load and
storage, or when to activate/deactivate system components. Moreover, AI models could detect unusual
behavior or possible component malfunctions.
- Actuation/Control: according to the AI's forecasting, control signals are directed the power management unit,
the harvester's mechanical components, or to the load unit to execute the optimized plan.
- Feedback Loop: A continuous monitoring of the system's performance and an update of the data pool, enable
AI models to enhance its predictions and strategies as time progresses, known as adaptive learning.
5. Benefits of AI in Energy Harvesting
5.1. Tangible enhancements in energy production and efficiency
The implementation of AI models in energy harvesting areas has continually proven significant and databacked improvements in energy production and the total system performance. These improvements achieve
transformative advancements, frequently functioning as a force multiplier that brings current technologies
nearer to their maximum theoretical efficiency. In the domain of solar photovoltaic, AI models enhance the
overall energy generation by up 20% through precisely forecasting solar panel output and allowing dynamic
adjustment [72]. Studies indicated an enhancement of the total energy harvesting by 10-15% compared to
traditional fixed-tilt systems using machine learning algorithms [73]. Regarding solar dual-axis tracking
systems, the integration of AI enhances the yearly energy production by 41.1%, demonstrating the powerful
effects of strategic positioning and adaptive control [74]. The optimization of solar installation using AI
increase the energy yield efficiency by 15-20% compared to conventional systems [74]. In the field of wind
energy, an increase of energy output by up to 20% owing to the role AI in real-time adjusting the turbine
settings to accommodate shifting wind conditions [65]. AI is not just offering incremental benefits but is
substantially advancing their overall performance. Thus, AI play an essential role in enhancing the potential
of current and future energy harvesting technologies, positioning them as more competitive and scalable
solutions within the global energy market
5.2. Economic Advantages: Cost Reduction and ROI
The integration of AI technologies in energy harvesting landscape transcends improvements in system
performance alone, yielding considerable economic returns via a considerable cost reduction and rapid
returns on investment (ROI). These economic advantages play a key role in enabling widespread adoption
and commercial viability of renewable energy technologies. for instance, the analysis of operational data to
predict equipment failures enables a proactive maintenance, sharply lowering the frequency of unexpected
outages and unplanned maintenance expenses. In the case of solar energy, the use of AI minimizes the
maintenance cost by 30% and a reduction of the total maintenance cost of 25-35% compared to established
reactive procedures [64]. The implementation of predictive maintenance cuts maintenance expenses and
lengthens equipment longevity, further decreasing future capital outlays [75]. AI-optimized solar installations
have the potential to add $5,000 to $8,000 in annual income for medium-sized solar farms, with the return
on investment generally manifesting over a brief timespan of 2-3 years [76]. AI ids in lowering total operating
expenses through reducing waste, digitizing regular operations, and enhancing resource distribution
throughout the energy system. This leads to minimize the levelized cost of energy (LCOE) per watt,
improving the economic viability renewable energy relative to conventional energy sources. Additionally,
the application of AI in energy harvesting system can considerably affect the market competitiveness and
investment attraction of renewable energy projects. This could be a critical role in scaling up renewable
energy infrastructure universally, since it improves the "bankability" of projects and establishes them as
viable competitors to conventional energy sources
5.3. Enhanced System Reliability, Durability, and Lifespan
AI is crucial in greatly improving system reliability and longevity of energy harvesting systems. This is
mainly accomplished by its advanced predictive capabilities and enhanced operational strategies. The
predictive maintenance offered by AI facilitate early detection of possible system malfunctions, resulting on
reducing expensive outages and cutting total maintenance costs. This results in ensuring sustained operation,
particularly important for remote or off-grid energy harvesting applications where on-site maintenance is
hard to perform and reliability is critical [2]. AI-driven systems have achieved notable improvements in
accuracy of fault identification, attaining up to 95% for solar installations, and can minimize system
downtime by up to 30% [38]. Moreover, AI could prolong the operation lifespan of energy harvesting
devices. Some studies indicated that AI could extend the effective lifetime of device 'indefinitely' till the
energy conversion device itself physically deteriorates, possibly increasing service life from 5-10 years up to
30 years [77]. For instance, a hybrid AI solar dual-axis tracking system indicated a 60% enhancement in
battery lifetime owing to its optimized energy management system, which enhances charging and discharging
cycles driven by real-time information [78]. These enhancements foster increased operational resilience and
a smore sustainable used of resources over the long term.
5.4. Contributions to grid stability and overall sustainability
AI acts as an essential factor for incorporating highly variable renewable energy sources within the current
electrical grid, radically reshaping it into a more resilient, smart, and sustainable system. The conventional
grids, crafted for reliable, centralized energy production, encounter some difficulties in handling the variable
characteristics of wind and solar power. AI responds to this by delivering sophisticated prediction
capabilities, real-time control, and demand response management features. AI- driven predicting tools and
grid-balancing algorithms empower energy providers to optimize supply-demand balance more efficiently,
resulting in lowering dependance on fossil fuel backups and maintaining reliable grid operation. AI
algorithms can forecast energy needed and automatically regulate electricity flow in response to demand
fluctuations, enhancing performance and reducing waste across the grid. This real-time flexibility is essential
for sustaining balance in a system progressively reliant on decentralized and intermittent energy sources.
Additionally, the integration of AI in factory processes can minimize energy consumption, waste, and carbon
emissions by 30-50% [79]. This results in reducing the environmental impact of energy generation and
consumption. AI can address the built-in variability of renewable sources, enabling higher penetration into
the energy mix while maintaining grid stability. This lead to optimize individual energy harvesting systems
and shift the whole energy grid in a smart grid that can respond dynamically to fluctuating distributed
generation, which is a fundamental prerequisite for achieving a sustainable energy future.
6- Challenges and limitations
Despite the numerous benefits of using AI in energy harvesting, several barriers should be addressed to ensure
its effective and widespread adoption.
- Data-Related Challenges: it involves data availability and quality, data management and integration, and lack
of standardized datasets. AI models, especially deep and machine learning, depend significantly on large
volumes of high-quality data for training, and gathering adequate and reliable data can be difficult [80]. This
is attributed to factors like limited sensor deployment, data variability, and noise and inaccuracies. Energy
harvesting systems, particularly in remote or distributed environments, might experience restricted sensor
deployment, leading to a sparse data. Renewable energy and thermal gradients are highly variable, resulting
in diverse and intricate datasets. Additionally, sensor data can be unreliable or imprecise due to environmental
factors, sensor constraints, or communication problems. In data management and integration, energy
harvesting system usually include data from different sources, such as sensors, environmental monitoring,
and historical records, Integrating and managing this diverse data can be challenging and demand
considerable effort. Regarding lack of standardized datasets, the absence of standardized datasets for specific
energy harvesting applications hinders the development and comparison of AI algorithms.
- Computational and Resource Constraints: some AI algorithms, particularly deep learning models, are
resource-intensive (energy consumption, water use for cooling) and demand substantial computational
energy footprint (carbon emissions and electronic wastes) of AI systems themselves. For instance, a single
hyperscale AI data center requires hundreds of thousands of gallons of water per day and consume 100-1000
megawatts of electricity, which similar to the power usage of a medium-sized city. This poses a challenge
for resource-constrained energy harvesting devices, and create a considered strain on existing energy grids
[20]. Observations indicate it causes slowdowns in reaching clean energy supplies and, in some cases, has
compelled regional utilities to restart decommissioned coal plants to handle the surging loads from data
centers, resulting in undermining local climate targets and optimized grid performance. This represents a
significant 'paradox of AI for green energy' defined as the very mechanism developed to improve energy
efficiency and speed up decarbonization is concurrently turning into a major energy consumer, potentially
diminishing its environmental benefit if not guided by sustainable management principles. Thus, the
environmental footprint of AI might cancel out some of the benefits it supports in renewable energy, if no
considered innovations in energy-efficient AI hardware, optimized algorithms that consume less energy, and
the extensive implementation of green computing practices. This requires a concentrated focus on
"sustainable AI" development alongside transparent disclosure of AI's environmental impact to maintain
accountability
- System Integration and development: the incorporation of AI algorithms with existing energy harvesting
system and infrastructure can be challenging, particularly if these systems were not originally built for AI
integration. Moreover, the integration of AI solutions might necessitate specialized hardware, like processor
or microcontrollers with adequate processing power, which might not be easily accessible or compatible with
current systems. Also, implementing AI solutions on a large scale or distributed energy harvesting systems
can be difficult due to the requirement for reliable communication networks, data management infrastructure,
and system maintenance.
- Model Development and Validation: there is a challenge in choosing the most suitable AI algorithm for a
specific energy harvesting application and fine-tuning its parameters. Also, it is vital for AI models to
generalize well to new data and different operating conditions to ensure their reliable performance in realworld energy harvesting systems. Moreover, the necessity of validation and testing to guarantee the accuracy,
reliability, and robustness of AI models prior to their deployment in critical energy harvesting applications
is considered as a challenge. Additionally, some AI models, including deep learning models, could be 'black
boxes', leading to challenges in understanding how they make their decisions. This absence of transparency
can impede adoption of AI in applications, especially in contexts where trust and reliability are crucial.
- Economic and Regula Factors: integration of AI technologies in energy harvesting systems can incur
considerable initial expenses, such as software, hardware, data collection and staffing costs. Moreover, the
creation and implementation of AI technologies necessitate expertise in specific areas like machine learning,
data science, and software engineering. A lack of qualified personnel can impede the adoption of AI in the
energy harvesting sector.
- Regulatory Factors: In terms of regulatory and standardization, there is a shortage in well-defined regulatory
frameworks and industry norms for AI in energy harvesting and the intrinsic challenges associated with
integrating emerging AI technologies into existing, heterogeneous energy systems an create ambiguity and
obstruct investment in this sector. The existing regulatory and policy framework supervising smart grids and
AI application is often disjointed and outdated, challenged by the swift evolution of emerging AI
technologies. This lack of a cohesive regulatory framework generates ambiguity for developers and investors,
possibly impeding the large-scale implementation of AI-powered solutions. The implementation of AI
systems with Aging electrical grid systems, which usually depends on different protocols (such as
Supervisory Control and Data Acquisition (SCADA), Advanced Metering Infrastructure (AMI), and
Distributed Energy Resource Management Systems (DERMS)) Involves considerable complexity. The
absence of industry-wide standards for interoperability can result in considerable barriers to effective
integration, delaying implementation and increasing costs. Additionally, since smart grids expand in
interconnection and dependence on digital technologies and AI for real time management, they concurrently
face heightened vulnerability to complex cybersecurity threats and vulnerability. A failure or attack on critical
energy infrastructure systems may result in devastating outcomes, highlighting the importance of strong
security protocols in conjunction with technological integration. The gap between the fast speed of AI
technological innovation and the slower, Careful and systematic regulatory frameworks causes a major
obstacle to secure, fair, and broad AI integration in the energy sector. This situation requires proactive policy
development encouraging industry-wide collaboration on common standards and protocols and putting in
place strong cybersecurity measures to close this gap. The full capabilities of AI could be thoughtfully
adopted into vital energy infrastructure, only with such coordinated collaboration
7- Future Directions
The integration of artificial intelligence (AI) into energy harvesting is set to revolutionize the field, enhancing
efficiency, flexibility, and range of applications. In the field of materials discovery and optimization, AI, especially
machine learning, will be essential in developing innovative materials with improved thermoelectric, piezoelectric,
and triboelectric properties. This will be done through forecasting the performance of materials through advanced
simulation techniques and machine learning models and optimizing materials composition and nanostructure for
optimal energy conversion efficiency. And, investigating new categories of materials like metamaterials and 2D
materials, for energy harvesting. Moreover, Future research may emphasize on creating AI-driven self-healing
materials that can sustain their energy harvesting efficiency over long durations. There is also future direction in
intelligent system design and optimization. AI will play a crucial role in the optimization of the configuration of energy
harvesting devices that adapt to fluctuating energy source availability. Additionally, AI-powered control algorithms
will be implemented to optimize energy collection under variable conditions. In term of multi-source energy
harvesting, AI could enable the combination of various energy harvesting sources (e.g., solar, thermal, vibration) to
develop hybrid systems that enhance reliability and power output. Moreover, AI will play a crucial role in establishing
intelligent energy management system with an ability to forecast energy demand and enhance energy storage
approaches, allow a seamless integration of energy harvesting systems with smart grids, and prioritize energy
distribution to critical loads. The future direction will focus on AI enhanced device and system miniaturization by
supporting the design and optimization of micro and nano-scale energy harvesting devices for supplying power nanosensors and microelectronics. In addition, future direction will emphasize in incorporating energy harvesting devices
with electronic circuits and sensors using AI-assisted design tools. Moreover, AI will aid in creating flexible and
wearable energy harvesting systems that adapt to the human body or other curved surfaces, enabling new applications
in healthcare and personal electronics. Future research will also investigate the use of edge computing and federated
learning to tackle the computational limitations of energy harvesting devices. Edge computing allows the local
performance of AI on the device, minimizing the necessity for data transmission to the cloud. Federated learning
enables AI models to allow AI models to be trained on decentralized data sources while maintaining data privacy. The
use of AI enhanced energy harvesting will be expanded to be used in powering the next generation of IoT devices,
biomedical devices, and space exploration.
8- Conclusion
The escalating global demand for sustainable energy, responding to the urgent call for decarbonization and improved
resource efficiency, requires profoundly innovative approaches to energy production and management. As part of this
ongoing transformation, ambient energy harvesting systems, crafted to harvest pervasive energy coming from sources
such as kinetic motion, thermal gradients, and radio frequency signal, are establishing themselves as foundational to
a self-reliant future. The paper has investigated the transformative potential of incorporating AI with these ambient
energy harvesting systems, showing the role of AI in optimizing the harvesting process, drastically in improving the
operational efficiency, reliability, and cost effectiveness of energy harvesting and utilization.
Driving this shift is AI's exceptional capability to improve energy capture in various aspects. AI models are
demonstrating essential in the enhancement of energy capture design, evolving beyond classic trial-and-error
strategies. AI can effectively identify the most optimal materials, enhance sophisticated configurations, and determine
accurate dimensions owing to advanced machine learning models, resulting in enhancement of the performance of
energy conversion. For example, AI- -powered material discovery platforms can promptly analyze hundreds of
thousands of chemical compositions, speeding up the discovery of new thermoelectric materials able to effectively
transform waste heat into electricity, significantly minimizing discovery timelines from years to few months. In
vibrational energy capture, genetic algorithms and deep neural networks are used to evaluate optimal electrical and
mechanical factors for piezoelectric system, maximizing energy production and ensuring resonant peak alignment.
Additionally, machine learning models are reshaping energy prediction and management through precisely forecasting
the provision of energy from intrinsically fluctuating ambient sources. In the area of solar photovoltaic, AI-driven
tools attain forecasting rates within noticeable 2-3% margin of current production values, a considered enhancement
over traditional techniques. These approaches process extensive data collections covering real-time weather patterns,
historical solar radiation, wind speeds, and delicate oscillation patterns, leading to a more effective energy
management and storage methods. This predictive ability enables for smart energy storage enhancement, allowing for
energy to be stored and delivered on demand, resulting in greater stability for grids and minimizing dependence on
rigid backup power systems.
In addition to static performance tuning and forecasting, AI-driven systems can adaptively adjust the functioning of
Energy harvesters in real time, modifying behavior based on environmental variability and variable demand levels. In
the field of solar energy, hybrid AI dual-axis tracking systems have shown a noticeable enhancement of yearly energy
output by 41.1% through real-time adjustment of panel angles in response to real-time environmental parameters, such
as temperatures, and cloud cover. In wind energy, AI algorithms streamline dynamic tuning of turbine configurations,
including blade pitch and yaw angle, adjusting based on fluctuating wind conditions, resulting in enhancing energy
generation by up to 20%. This responsive adjustment secures optimal energy harvesting and usage, enhancing the
output from each relevant environmental factor.
Lastly, in the area of detection and maintenance, AI models are revolutionizing reliability by spotting irregular
patterns and predicting potential breakdowns in energy capture systems. This transition from reactive to proactive
maintenance is realized by ongoing monitoring of live sensor inputs, resulting in timely identification of minor
irregularities or deterioration signals. For example, AI-driven drone-based image analysis can quickly analyze vast
solar farms, detect tiny fractures and overheating areas, or soil with up to 98% precisely, considerably minimizing
system downtime and maintenance expenses by up to 30%. Likewise, AI evaluates the status of material aging of
wind turbines, precisely forecasting the duration of their continued usability and facilitating early responses that
enhance system reliability and increase functional lifespan
In summary, the implementation of AI into ambient energy capture systems signifies a major step forward in
achieving a more resilient, performance, and sustainable global energy future. AI is enabling exceptional potential in
capturing the scattered energy resources around us through optimizing design, improving forecasting, allowing realtime management, and reshaping maintenance strategies. This lays the foundation for a new era of self-sustaining and
smart energy infrastructure.