Applications of Artificial Intelligence in Solar-Powered Electrical Systems
November 25, 2025
The integration of artificial intelligence into solar power systems indicates an immense change in renewable power design, optimization, and management. As the solar energy share in the global electricity generation is increased, the demand for smart, data-hinged control and prediction mechanisms becomes overwhelmingly critical. AI applications in smart solar-powered systems provide the necessary means to address various challenges that accompany solar power generation and distribution. The main challenges are mainly variability, intermittency of the generated power and also nonlinearity of the system. AI methods overcome such issues through predictive modeling, adaptive optimization, and autonomous decision-making (Teta et al., 2024; Mishra et al., 2023).
Machine learning and deep learning algorithms are powerful tools that are able to extract complicated spatiotemporal attributes from meteorological and operational datasets. These methods are very commonly used for solar irradiation prediction, energy dispatch strategy improvement, and enhancement of photovoltaic (PV) system reliability (Guermoi et al., 2022; Franco et al., 2025). Moreover, machine learning techniques are also employed for maximum power point tracking (MPPT), fault detection, and predictive maintenance in order to increase PV installations’s overall efficiency and lifespan (Abouzeid et al., 2024; Rukhsar et al., 2025; Ledmaoui et al., 2025).
AI merges with the Internet of Things (IoT) and spans the smart solar-powered systems by helping real-time monitoring, fault diagnosis, and remote optimization through various interconnected sensor networks (Kaiss et al., 2025; Wali & Khan, 2021). These AI-oriented IoT systems still face many issues regarding data quality, interpretability, and model generalization, all of which limit the large-scale deployment of such systems in solar energy systems control (Ukoba et al., 2024; Afridi et al., 2021).
The increasing amount of research in AI applications in solar energy systems is proof that AI is an important actor in the advancement of solar technologies toward more efficiency and adaptability. By exploiting such techniques, the field of solar energy has already seen progress toward an era of self-regulating smart solar power control systems that are able to independently forecast, optimize, and maintain their performance under dynamic environmental conditions. This article discusses some of the key applications of AI and their associated challenges in solar energy systems, including: forecasting direct irradiation, dispatching and energy management,Internet of things (IoT) ,fault detection, and predictive maintenance.
1. Outline of Machine Learning Algorithms, Techniques and Methods
Machine learning (ML) and artificial intelligence (AI) are increasingly integrated into solar-powered energy systems, as they help to improve the overall system’s performance. Fig. 1 shows a general classification of machine learning techniques. Solar energy systems leverage machine learning’s ability to learn from historical and real-time data in order to produce more adaptive responses to various fluctuating environmental conditions. For instance, supervised learning methods (that learn a mapping from inputs to known target outputs by using labeled data), like support vector regression, are used for prediction tasks, while unsupervised learning techniques (that discover possible structure in unlabeled data) help discover patterns in system behavior. Reinforcement learning methods enable real-time decision-making. Deep learning models (e.g., ANNs, CNNs, and RNNs) manage complicated spatial and temporal data while improving the accuracy of classification and/or forecasts. Alongside modeling techniques, selecting the right input (features) plays an equally critical role in shaping model performance and reliability. Feature selection identifies the most relevant inputs of the model and therefore plays a key role in improving model efficiency and accuracy. Below is a quick review of feature selection, machine learning, deep learning, and meta-learning techniques.
It is worth mentioning that in this context, ‘modeling’ refers to making a data-driven description of how a solar powered system behaves under dynamic environmental and operational conditions.
Fig. 1. Machine learning and its subfields
1.1. Feature Selection
Feature selection is a process that helps reduce noise and enhance prediction accuracy by choosing the most input variables for the predictive model. In this regard, we use various machine learning algorithms (Castangia et al. 2021). Fig. 2 shows a simple diagram explaining how feature selection works. The first row represents all features in the data set. The second row are the most important features identified by the feature selection algorithm. The last row is the pruned dataset, which only encapsulates the most influential features.
In this article, feature selection is treated separately to differentiate it from direct prediction. With this foundation, we can now explore the primary AI modeling approaches used in solar applications.
1.2 Common AI Modeling Methods in Solar-Powered Systems
There are many methods involved in modeling solar energy systems. Below, the article reviews the most common algorithms and their categories as per the literature.
1.2.1. Machine Learning Techniques
Although this article concedes that deep learning methods are commonly classified as subsets of machine learning techniques, the two groups are differentiated so that a more focused section can be assigned to each method. Therefore, the term ‘machine learning methods’ refers to techniques that, in contrast to deep learning methods, classify and forecast without using multi-layer neural networks for automatic feature learning, which results in using less data for training and more transparency.
Different machine learning methods are used to model the non-linear patterns observed in weather, including but not limited to:
- Support Vector Regression (SVR): predict continuous/discrete values by finding a hyperplane that best fits the data within an ε-insensitive margin. SVR focuses on maximizing the flatness of the function while allowing certain deviations (errors) controlled by ε and regularization parameters (Khanna & Awad., 2015)
- Extreme Gradient Boosting (XGBoost) and CatBoost : XGBoost and CatBoost are gradient boosting algorithms that make strong predictive models by combining the outputs of many weak learners in sequence to minimize errors. Both XGBoost and CatBoost commonly use decision trees as their weak learners. XGBoost is usually optimized for speed and performance, while CatBoost is better suited to handle categorical features natively to reduce overfitting (Chen & Guestrin, 2016).
Fig. 3. Deep learning is a subset of machine learning algorithms which itself is a subfield of artificial intelligence.
1.2.2. Deep Learning
Artificial neural networks with multi-hidden layers enable deep learning techniques to automatically learn data patterns and features. However, it is true that deep neural network architecture may require data cleaning and feature engineering occasionally. These methods typically require more data and computational power to be trained compared to conventional machine learning methods such as regression methods but they are able to capture complex non-linear patterns.
- MLP is a basic type of artificial neural network made up of layers of neurons—an input layer, one or more hidden layers, and an output layer. MLPs process data in a straight line from input to output (no loops) and are suitable for simple pattern recognition tasks, although they struggle with time-series or spatial data as they treat each input independently (Pinkus et al., 2008).
- RNNs are made to handle sequential data, like time series or speech. They leverage loops in their network structure that allow them to “remember” past information. This memory makes them suitable for predicting time series. However, they struggle with issues like vanishing gradients (Sherstinsky, 2020).
- CNN is tailored for processing grid-like data, such as images or spatial weather maps. CNNs use convolutions to automatically detect patterns like edges or textures. In solar energy, they can extract spatial patterns from satellite imagery or high-resolution sensor grids (Yin et al., 2017).
Fig. 4. Structure of a one-block RNN elapsed over time
1.2.3. Meta Learning
Meta-learning, which is also called “learning to learn,” is a method or process in which an additional AI model learns the best way to combine or tune several other AI prediction models. Traditional methods depend on a single forecasting algorithm. However, in meta learning, the main algorithm gathers the strengths of multiple models with their own biases and then uses a higher-level machine learning algorithm to decide if each model’s output should be trusted or not. A Neural Turing Machine (NTM) relates to meta-learning by enabling a model to store, retrieve, and adapt knowledge from past tasks using an external memory, allowing it to “learn how to learn” across different problems. Faradonbeh & Safi-Esfahani, 2020 present the architecture of a Neural Turing Machine (NTM) that consists of a controller, which is an RNN neural network. The controller engages with an external memory matrix by using read and write heads. The read heads retrieve information using weighted sums, while the write heads modify the stored data in memory through erase and add operations. This process enables the model to learn algorithmic tasks and store long-term dependencies. Fig. 5 shows the structure of a Neural Turing Machine (NTM).
Fig. 5. the architecture of a Neural Turing Machine (NTM)
The author purposefully excludes algorithm details like derivations, architectures, and hyperparameters from the scope of this work and simply mentions the algorithms where necessary, as this piece attempts to put focus exclusively on the applications of AI in solar-powered systems. This section classified AI modeling techniques that are applied in solar energy systems into machine learning, deep learning, and meta-learning. While machine learning techniques provide more transparency and efficiency, deep learning methods uncover complex non-linear patterns from large datasets, and meta-learning algorithms improve a model’s performance by combining multiple models and adaptively optimizing based on their learning methods.
2. Solar Radiation Forecasting Using AI (AI Applications in Solar Energy Systems)
Direct solar irradiation (also called direct beam) plays a pivotal role in designing solar energy systems, and predicting it involves predicting direct normal irradiance (DNI). Direct normal irradiance (DNI) is the amount of sunlight that has reached the Earth’s surface without being scattered by the atmosphere and is given in W/m² . DNI is the piece of sunlight that is crucial for PV performance and grid operations. Accurate forecasting of solar irradiation is an essential part of designing a solar-powered system, as better predictions in this field directly enable the operators to plan energy use and reduce unexpected failures. However, because atmospheric conditions (e.g., clouds, aerosols, and humidity) are variable and non-linear, traditional models often struggle to produce reliable results (Teta et al. 2024). Therefore, AI models, with higher accuracy and less run-time, are needed to capture intermittency and stochastic behavior (Arman et al., 2023).
Moreover, Mishra et al. 2023 declare that improved forecasting models enable more accurate photovoltaic production planning and a smoother approach toward grid scheduling. Furthermore, more accurate weather forecasting means increased efficiency for the system and superior economic performance (Mishra et al. 2023). The following section reviews the case studies and challenges associated with using artificial intelligence for forecasting solar irradiation:
2.1. Forecasting Direct Solar Irradiation Case Studies
One of the main obstacles in this regard is choosing adequate data for the models. Weather datasets often contain many possible predictors (features), yet only a handful of them should be put to use. Feature selection (as per Solano et al. 2022) is therefore emphasized in finding the most important environmental or operational variables from the large datasets. A common method for feature selection is to apply ensemble methods that consider weather-oriented variables such as temperature, humidity, wind speed, and their previous values in order to determine the strongest and most defining irradiance predictors (Solano et al. 2022). Ensemble learning often boosts modeling algorithms by methods such as model averaging and stacking, where predictions from several models are aggregated by a meta-learner for enhanced accuracy and generality. Huang et al. 2021 introduce a straightforward approach for predicting direct solar irradiance. Their method starts with collecting meteorological data such as temperature, humidity, and sunshine duration. Then the data is preprocessed, followed by a correlation analysis for feature selection. The cleaned processed data is then fed to 3 machine learning algorithms: Random Forest, Support Vector Machines, and Artificial Neural Networks. These algorithms are then trained to predict solar radiation levels. Their approach is shown in Fig. 6.
Support Vector Machines are recognized as one of the most effective, specifically when they are coupled with ensemble methods that handle the weakness of each individual model. For example, instead of training the model solely based on Support Vector Regression, practitioners often average predictions of SVR to obtain better accuracy (Solano et al. 2022). In the context of predicting direct solar irradiation, meta-learning methods are also useful as they help the system cope with abrupt changes in weather patterns (e.g. cloud cover or dust storms), which are commonly known to confuse individual models (Rai et al., 2025). In practice, a stacking-based meta-learner is trained on the prediction made by other models (such as linear regression methods, neural networks, or decision trees) and learns which models are able to produce the best results under various shifting conditions.
Meta-learning methods also help when it comes to direct irradiation forecasts, as they usually lead to a more stable model that can generalize predictions of solar irradiance over time. The resulting enhanced accuracy allows for better photovoltaic power scheduling, energy dispatching, and grid reliability in later design stages (Nwokolo et al. 2022). For instance, stacking methods implement a second model trained on the outputs of the first-layer predictors, which allows the AI model to mix different biases and therefore is able to make a more approved generalization, especially under abnormal weather conditions (Huang et al. 2021).
2.2 Forecasting Direct Solar Irradiation Challenges
Solar irradiance forecasting with applied AI and deep learning approaches faces various challenges. Deep learning techniques (e.g., MLP, LSTM, and GRU) seem very promising for making precise predictions of direct solar irradiance; however, these methods often need meticulous optimization and hyperparameter tuning specifically when it comes to time-series forecasting and its relevant applications (Dhake et al., 2023). Moreover, spatio-temporal solar irradiance forecasting is accompanied by computational and mathematical complexity, specially when it is formulated as graph distribution learning problems (which is needed when ANNs such as Graph Neural Networks are involved) (Zhang et al., 2023). Hybrid approaches that integrate multiple algorithms (e.g., models, genetic algorithms, back-propagation neural networks, and adaptive learning mechanisms) try to address these limitations (Zhang et al., 2023). However, achieving reliable probabilistic forecasts and simultaneously balancing reliability, sharpness, and stability remains significantly difficult. The core problem is accurately capturing nonlinear relationships while managing seasonal and weather-dependent variations in continuous time-series data (Assaf et al., 2023).
Fig. 7. Challenges of direct Irradiation forecasting with AI
Solar irradiance forecasting relies heavily on selecting informative features and combining diverse modeling techniques, such as ensemble and meta-learning, to enhance predictive accuracy. In spite of the progress in deep learning and hybrid models, issues still persist in optimizing time-series forecasts due to the complexity of weather patterns and the need for reliable, adaptable forecasts under changing environmental conditions.
3. Maximum Power Point Tracking (MPPT) with AI
The ‘maximum power point’ of a photovoltaic panel is the module’s operating point (its corresponding voltage or current) that generates the maximum output power that the module is capable of. Fig. 8. The MPPT point is represented on the I-V figure of a typical solar panel, where Imp and Vmp denote the current and voltage values associated with this maximum power point.
In this regard, Maximum Power Point Tracking (MPPT) is a control method that helps photovoltaic panels operate at their maximum power output under varying operational conditions (Abouzeid et al. 2024). AI MPPT methods help approximate/forecast the MPP from sensor data, then command the duty cycle to reach the point of mpp quickly. AI-based MPPT methods are able to reduce oscillations and escape local maxima (partial shading) (Yap et al., 2020).
As the module’s optimal power point depends heavily on environmental factors such as irradiance, temperature, and load, MPPT techniques modify the module’s voltage/current to extract the maximum available power. MPPT algorithms are usually employed in power converters (such as DC-DC converters and charge controllers) to constantly adjust the duty cycle, ensuring that the PV module constantly experiences the ideal load conditions that are required for the PV system to operate at its MPP. Perturb and observe (P&O) and incremental conductance (IC) are two of the most commonly established methods used in MPPT due to their simple design; however, such methods struggle when they face varying environmental conditions, such as partial shading and daily temperature change. For this reason, more advanced MPPT methods are motivated through the literature (Ali et al. 2025).
Fig. 8. MPPT point on the I-V figure of a typical solar panel.
3.1. AI-Driven MPPT Case Studies
Recent studies have proven that adopting different artificial intelligence solutions to MPPT improves PV energy harvest when compared to traditional methods (Rukhsar et al. 2025). A study by Abouzeid et al. in 2024 presented and validated a data-driven MPPT controller based on an artificial neural network that is able to adjust the PV operating point in real time and achieved 98.16% tracking efficiency and a fast response of ~1.3 s. Moreover, reinforcement learning (RL) , namely Q-learning, is introduced as a technique for data-driven, adaptive control and decision-making under time-varying environments. For instance, Eyimaya, 2025, employed a reinforcement learning approach at MPPT. In their study, Eyimaya in 2025 introduced a controller based on Q-learning that outshines conventional methods in terms of accuracy and energy yield. This controller, without needing a predefined PV model, is capable of dynamically learning the optimal duty cycle. Many studies employ Hybrid AI schemes and the literature frequently mentions fuzzy logic controllers and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), where the interpretability of rule-based systems is combined with the learning capabilities of the neural networks so that the overall system remains insensitive to uncertain and nonlinear conditions. For instance, Udayanan et al. 2024 combined an ANFIS (adaptive neuro-fuzzy inference system) with a modified grey wolf optimizer that resulted in rapid MPP detection combined with high tracking accuracy. These case studies illustrate that MPPT methods that are based on AI are able to produce more effective responses to dynamic environmental factors and therefore they are able to increase the overall efficiency of the system when compared to traditional MPPT methods.
Fig. 9. MPPT Charge Controller Setup
3.2. AI-Driven MPPT Challenges
In spite of producing ideal results, there are still various issues when an AI based method is used for maximum power point tracking. Firstly, AI-based techniques hinge on a considerable amount of training data and accurate parameter tuning (Ali et al. 2025). Secondly, there is a risk of premature convergence when AI-driven techniques are employed in MPPT, especially if the conditions change beyond their training scenarios (Rukhsar et al. 2025). Also, using these algorithms may increase the hardware cost since they are more complex compared to traditional algorithms (Rukhsar et al., 2025).
Fig. 10. An MPPT Control System Diagram
Moreover, real-time limits and constraints and inference latency during rapid irradiance/temperature shifts result in tracking lag. Also, domain shifts across various module technologies, such as different inverter topologies, wiring, and local climate, can erode accuracy (Ali et al. 2025). Adding to this, sensor noise, missing data, and cyber-tampering issues can mislead the controller (Rukhsar et al. 2025). AI-driven MPPT techniques improve photovoltaic performance and efficiency by conducting fast, adaptive, and highly accurate tracking of the maximum power point under varying environmental conditions. However, their implementation is accompanied by challenges such as the need for extensive training data, sensitivity to domain shifts, and increased system complexity and cost.
4. Demand Prediction and Load Balancing
Electricity demand/load profile forecasting refers to all the methods that estimate the future power consumption over various time spans. Accurate demand predictions are critical to grid operators, as they help to balance electricity generation and consumption in real time and avert grid instability and blackouts (Franco et al., 2025). Load prediction is specifically in the case of solar power generation for three main reasons: Firstly, unlike conventional power plants, photovoltaic (PV) output is intermittent and depends on weather conditions, which complicates the alignment of load and generation. Secondly, solar power plants and other renewable energy sources have constituted 30% of global electricity generation (Franco et al. 2025). Thirdly, research studies declare that accurate demand prediction is especially noteworthy for sustaining balanced, robust performance in PV power systems since large-scale energy storage is still limited (Franco et al. 2025). In this context, predicting energy demand and production becomes necessary.
Traditional load forecasting techniques (e.g., statistical time-series models) experience difficulties when it comes to capturing the non-linear effects that widespread photovoltaic integration brings . For instance, traditional load forecasting methods struggle when rapid net generation fluctuates on cloudy vs. sunny days, or when households use their own solar power and consumption patterns change (Franco et al. 2025). These shortcomings motivate artificial intelligence methods to emerge as the key solution to modern demand forecasting. AI methods are able to learn from large datasets and manage complexity. In fact, recent advancements in AI enable adaptive energy demand forecasting, which yields more accuracy and that helps stabilize grid operations with intermittent renewables (Razak et al. 2025).
Fig. 11. Conventional inputs for Load Forecasting Models
4.1. AI-Integrated Demand Prediction Case Studies
Many studies have pursued using machine learning techniques to predict the electricity demand in renewable energy systems by using supervised learning techniques (e.g. decision trees, random forests and support vector machines) (Aquila et al., 2023). Fig. 12 is derived from the work of Asadi et al. 2023, in which they predicted consumption of electricity in Iran over 2021–2040 using ARIMA and exponential smoothing. They indicated that annual electricity consumption will grow from 284.8 TWh in 2019 to about 507.5 TWh by 2040 (+3.72% per year) with a MAPE of 1.44%. Short-term load forecasting can be improved by adopting transformers and attention mechanisms to focus on the most knowledge-rich time steps and channels (Lu et al., 2024).
Recent literature has increasingly utilized deep learning architectures due to their capacity to represent temporal structures. For instance, recurrent models (LSTMs and GRUs) can capture daily/weekly seasonality and intraday dynamics. Weather and irradiance nowcasts are fed into these algorithms so that they can approximate net-load swings (Dhaked et al., 2023). Further, because CNNs are able to analyze high-frequency load profiles and spatiotemporal weather fields for feature generation, it is also possible to pair CNNs with LSTMs to capitalize on both local pattern extraction and long-range temporal memory (Guo et al., 2025). Other hybrid pipelines are widely used too. In a study by Hu et al.,2025, clustering segments are used to segment heterogeneous consumers or categorize building types before training specialized deep models.
Fig. 12. Historical and forecasted electricity consumption in Iran through Holt (1990–2040) by Asadi et al. 2023.
4.2. AI-Integrated Demand Prediction Challenges
Demand forecasting using AI has several challenges . A significant challenge is the complexity of designing models, as there are an enormous number of possible optimal model architectures. Using auxiliary information like social media sentiment or weather information improves the quality of forecasts but makes the addition of these exogenous variables complicated (Henriques & Pereira, 2024). There is also a need for high-level data and expert skills to deploy and interpret AI models that are model-based. We must handle the risk of biased and non-transparent AI algorithms with extreme caution. There may also be sensitivity of the AI model’s performance to shifts in the behavior of the end-user or the sources of data such that the accuracy may suffer over time (Pan & Yang, 2016).
AI-based methods are increasingly used for electricity demand forecasting because they can model complex, nonlinear consumption patterns, especially under the changing uncertain conditions introduced by solar power integration. However, challenges such as model complexity, data dependency, and long-term accuracy under shifting conditions persist and create critical hurdles in their deployment.
5. AI-Driven Dispatch
Dispatching methods are strategies that define when and how power generated by PV panels or stored in storage units is delivered to meet electrical demand.
Dispatching methods coordinate PV generation and battery charge/discharge with the electrical grid, enabling services such as frequency regulation, peak-shaving, and ramp-rate control to ensure grid stability and efficient resource use; thus, they are pivotal to the system’s energy management. By storing excess solar energy during peak generation and dispatching it during high-demand periods, dispatching methods reduce the stress on the grid and minimize energy costs (Muriithi et al. 2021).
Dispatching methods are critical to islanded grids and off-grids as well as grid-connected systems since they are employed to balance PV output, battery storage, and backup generators in order for the system to meet the local demand.
AI‐driven methodologies improve solar dispatch mainly through accurate generation forecasting and integrating them with control frameworks. For instance, supervised learning models such as MLP, RNN, and LSTM produce valid day‐ahead photovoltaic output predictions. More specifically, recurrent neural networks like long short-term memory (LSTM) are suitable to learn long-term trends that are associated with weather data (Guermoui et al., 2022). Such predictions are employed to optimize battery storage and charge/discharge scheduling under market uncertainties (Sahoo et al. 2023). Moreover, many energy management systems (EMS) use intelligent control methods to preserve battery life by balancing the energy transition between solar generation and consumption (Zerk et al. 2023). Regardless of the project’s scale, deploying dispatching maximizes the share of renewable energy and improves power quality by adjusting the system to meet changing load demands and environmental conditions (Muriithi et al. 2021).
Fig. 13. Simple Dispatching Diagram
5.1. AI-Driven Dispatch Case Studies
Literature shows a spike in using AI solutions in dispatching strategies for on- and off-grid systems. Reinforcement learning, in particular, is a preferred approach among practitioners for battery management systems, as such models are able to extract the rich spatial/temporal patterns and features autonomously without human intervention. For instance, Muriithi et al., 2021 used reinforcement learning to coordinate a PV-battery microgrid’s battery schedule. Timing charge and discharge actions significantly reduced the system’s dependency on the utility grid. Reinforcement learning agents learn by interacting with the environment and working toward objectives such as cost savings or self-consumption over time. Another important set of modeling tools that are used in smart dispatching are artificial neural networks.
In particular, deep neural networks are most often employed to provide predictive information about solar generation or the demand profile and allow proactive dispatching decisions (Memon et al. 2025). Deep learning methods approach this modelling problem from a unique perspective, as artificial Neural Networks (ANN) are capable of delivering short-term forecasts that are adjusted to the local climate; therefore, ANNs can also directly be embedded into the control system. Jouili et al., 2024 introduced an ANN-based controller that is trained to perform maximum power point tracking and direct power flows between system components and functionally act as an intelligent ESM. Moreover, fuzzy logic controllers can be used to make a rule-based approach that is well-suited to cope with the uncertainty characteristics involved in solar production and consumption (Zerk et al. 2023). Fuzzy controllers do not require large historical datasets and produce “if-then” rules to dispatch energy. Fuzzy bases are commonly praised for their robustness to variable conditions (Zerk et al. 2023).
Reviewing existing literature, it can be inferred that across various case studies, dispatch methods that are AI-based are well able to outperform traditional strategies, as they achieve better results such as smoother PV power output and lower operating costs.
5.2. AI-Driven Dispatch Challenges
Introducing AI-enabled dispatch control into solar PV systems comes with its set of challenges:
- Computational complexity and training data requirements: The training of AI algorithms often requires extensive simulations and massive data. For example, a controller that is using reinforcement learning techniques, needs many iterations to converge on a final policy. Further, multiple simulations under a vast range of conditions are needed to ensure the policy will generalize well outside the scope of the data it is trained on (Hanif el at., 2025).
- Lack of Transparency: Deep neural networks act mostly as “black boxes,” meaning that it would be hard for engineers to translate the dispatch decisions based on deep learning methods and guarantee that all grid constraints are respected under every possible condition (Ejiyi et al. 2025). It is worth noting that, although fuzzy logic-based controllers are considered more interpretable due to their “if then” rules, parameter tuning a fuzzy rule set for large systems can be very labor-intensive and there is always the possibility that the controller may not even scale well (Ejiyi et al. 2025).
- Integration and reliability issues: AI models heavily rely on the inputs to be accurate and in real-time and therefore, if the input data is erroneous or delayed, the output dispatch decisions would be suboptimal. Moreover, many early AI dispatch methods often neglected system factors such as battery degradation, which is crucial to the reliability of real-life systems (Muriithi et al. 2021). To add more, an aggressive dispatch policy that aims to maximize short-term performance could also reduce battery lifespan, specifically if wear-and-tear costs aren’t accounted for (Hanif et al. 2025).
AI-based dispatch strategies improve the coordination of generation, storage, and consumption compared to traditional dispatch methods. These methods work by predictive and adaptive control that leads to more efficiency and reliability of energy systems. Although, various challenges such as data demands, system integration complexity, and model interpretability must be addressed when these techniques are involved.
6. AI-Based Predictive Maintenance
Predictive maintenance applies data-driven monitoring techniques to predict failures and also plans to take action only when necessary. Using predictive maintenance causes downtime to decrease and increases energy yield compared to fixed-interval maintenance (Ledmaoui et al., 2025). The main difference between predictive maintenance and forecasting AI tools is that, while forecasting estimates the output of the system, predictive maintenance estimates when and how much the system is going to deviate from its normal work flow. Field reliability analysis studies show that PV-powered systems are vulnerable to a range of degradation issues and faults and highlight PV modules and inverters as the most dominant sources (Oviedo et al. 2023)
. AI-centered predictive maintenance uses high-frequency SCADA data, thermographic data, and environmental data to detect anomalies and approximate the remaining useful life (RUL) of each system component and therefore helps to enable targeted cleaning schedules and firmware updates. Many studies, such as Ledmaoui et al. 2025, list the benefits of the shift from diagnosis to prognosis that has resulted from using artificial intelligence tools in predictive maintenance. The following steps show the general pipeline of predictive maintenance using AI (Hector et al. 2024):
- Data acquisition from sensors (inverter side, meters, weather stations)
- Data stream cleaning and alignment
- Model training
- Failure prediction
- Turning model outputs into work order using thresholds/risk assessment
It is worth mentioning that Ledmaoui et al. 2025 declare that as solar-powered systems scale up, approaching predictive maintenance through machine learning tools becomes a necessity rather than an option.
Fig. 14. Predictive Maintenance Diagram
6.1. AI Case Studies in Predictive Maintenance
Artificial intelligence empowers predictive maintenance in solar PV- Battery storage systems as it is based on using machine learning to predict power output and employing metaheuristic optimization to tune system parameters. Short-term output power forecast models that are developed with algorithms such as support vector machines and ensemble learning, establish expected generation profiles in order to detect deviations indicative of faults or degradation (Henriques & Pereira, 2024).
Hybrid grey wolf and cuckoo search algorithms determine optimal battery sizing and charging parameters, which underpin accurate state-of-health estimation and maintenance decision-making (Nguyen, 2023). They are frequently used for the purpose of improving control parameters or optimizing the convergence of the predictive model and its efficiency. Multi-criteria evaluation frameworks integrate technical performance indicators, environmental metrics, and maintenance cost analyses to prioritize maintenance actions and enhance system reliability, bolstered by comparative studies on battery charging strategies (Abhulimen & Ejike, 2024).
6.2. Predictive Maintenance Challenges
As auspicious as predictive maintenance via AI, it still has three major challenges. The main challenge in this regard is that real fault events are relatively rare compared to normal operating conditions, so gathering the required data and labeling it is often inconsistent across operators. However, constant efforts have been put into cataloging reliability datasets to standardize benchmarks (Oviedo et al. 2023). Secondly, models trained on one solar fleet cannot truly generalize to new geographies, irradiance levels, and temperature ranges (Marangis et al. 2024). Thirdly, transparency and explainability are other issues, as sincere evidence is needed when truck tolls and warranty claims are justified (Rana 2025).
It is true that different communities are addressing these issues by using open datasets and clearer methodological separations between detection and diagnosis; on the other hand, there is still a persistent need for more standardized taxonomies, cost-based evaluation metrics, and explainable AI (methods that seek optimize transparency and interpretability in the otherwise complicated AI-operated solar energy systems) in order to make predictive maintenance trustworthy at a large scale (Chen et al. 2025).
Fig. 15. Challenges of predictive maintenance
Predictive maintenance can be carried out using AI-based methods, as they enhance the reliability and efficiency of solar PV systems by predicting fault time and location and optimizing maintenance schedules using sensor data. Despite the advantages of this approach, key challenges—the scarcity of labeled fault data and poor model generalization across diverse environments—still stall progress.
7. Fault Detection with AI
In solar-powered systems, fault detection is necessary because if an anomaly goes undetected, it results in output degradation and safety risks and is even able to trigger cascading failures. Salazar-Pena et al.,2024 notes that if technical faults go unidentified, power losses of a magnitude of 10%‑70%, accompanied by an average energy production decrease of 25%, can happen partly due to the intermittent nature of PV-powered systems. Due to the inability of traditional human-based or threshold-based methods to manage the operational conditions in a PV system, AI models are continually developed to monitor multi-modal inputs, identify system deviations, and detect defects automatically. As using AI techniques in fault detection is believed to: lower maintenance costs while increasing system uptime, and extend system’s overall lifespan, employing such methods has become a necessity rather than an option. It is also worth noting that reliable and varied data inputs, such as electrical profiles, thermal signals, and environmental factors, are needed so that the AI-based models can perform a meaningful fault detection.
Fig. 16. Aftermath of an undiagnosed Electrical Fault
7.1. Fault Detection Case studies
Salehimehr et al. 2024 built a low-voltage microgrid model and mixed a regression tree with compressed sensing for fault detection while using a long short-term memory (LSTM) network to determine fault location. 93% accuracy in fault location and 1 ms time for fault detection are reported. In another study, Liu et al. 2023 used random forest to detect and locate internal short circuit faults in lithium-ion battery energy storage systems. They inferred that their proposed system is well suited for deployment in real-life situations. Gaaloul et al. 2025 deploy random forest and KNN for current and voltage prediction, respectively. In their proposed method, the differences between predicted and measured signals drive fault signature. An R2=0.995 was reported for validated daily fault identification.
7.2. Fault Detection Challenges
Although AI deployment for fault detection is growing, PV systems are still challenged by issues such as
- Interpretability, specifically motivating physics validated XAI (explainable AI) (Aksoy S. 2025)
- Synthetic data generation: there is a lack of real-world data in regards to rare faults and therefore synthetic data needs to be generated to cover it (Lin et al. 2025)
- Cross-domain drift happens when an AI model that is trained on data gathered from one solar farm might not work as well under another setting. For example, a model trained based on the data gathered from a solar farm in the south of France probability would fail to work well on a PV farm in Iran that use different modules and sensors (Lin et al. 2025)
AI-based fault detection in solar systems detects anomalies early, and as a result, increases reliability, reduces energy losses, and extends system lifespan. However, challenges such as limited real-world fault data, domain transfer issues, and the need for physics-informed explainability hinder trustworthy deployment of such methods across a diverse range photovoltaic scales.
8. Integration of AI with IoT for Solar Power Systems
The integration of AI with IoT (Internet of Things) in solar power systems enables an unprecedented level of real-time monitoring and control (Tung et al. 2024). While IoT sensors gather data (irradiance level, ambient temperature, module currents and voltage, and dust accumulation), AI algorithms review these data streams to identify and/or predict performance atrophy, output generated power potential, and also to make control decisions such as starting dynamic tracking, adjusting inverter settings, or PV module selective pruning (Al Humairi et al.2025). Further, AI is used in IoT to detect, forecast, and locate incipient anomalies or faults (e.g., partial shading, inverter drift), which enables the system to avoid unplanned downtime.
Fig. 17. An IoT system sketch
8.1. AI-Integrated IoT Case Studies
The deployment of AI in IoT is highlighted in research literature. Verma et al., 2022 declare that machine learning models trained on historical/synthetic datasets can identify PV faults such as partial shading and string mismatches specifically when sensor data stray from normal operating conditions. In this regard, Wali & Khan, 2021, present a hybrid approach for detecting incipient faults that mixes XGBoost with a model-based signature technique. Their proposed method prioritizes explainability, enabling operators to understand the reasons behind a fault’s flagging. Studies have also reported hybrid deep learning techniques used for fault detection in PV systems can reach diagnosis accuracies over 90% (Salazar-Pena 2024).
Convolutional Neural Networks (CNN) can be used to infer spatial weather patterns from satellite images that are effective in multi-hour predictions. In another study, Teta et al.,2024 demonstrate a CNN model that, because of its lightweightness and feasibility for deployment on hardware with limited connectivity, can effectively reduce latency in fault detection in grid-connected photovoltaic systems. These AI-IoT systems are able to form adaptive responses so that performance degradation in one component does not overflow to a total-system failure.
A major advantage of combining AI with IoT in solar systems is that the integration enables architectures like virtual power plants (VPPs) to ease coordination with grid operations and energy markets. For instance, Kaiss et al.,2025 underscores the emerging need for robust control strategies in VPP and virtual aggregator technologies
after presenting a review of such systems. Liu & Gao, 2025 review how AI methods are useful in resource aggregation, optimal scheduling, and market participation in VPP frameworks, which, as a result, help coordinate distributed energy resources under uncertainty. Moreover, Sawilam et al., 2025 inspects how integrating IoT control and VPP logic can enhance grid stability and renewable energy source integration in real-life scenarios.
8.2. AI-Integrated IoT Challenges
Despite all this potential, according to Kaiss et al. (2025) and Liu & Gao (2025), challenges persist:
- Energy systems must resist interoperability among heterogeneous distributed systems.
- Cybersecurity concerns arise from connected devices, especially in large-scale systems
- The black-box and unexplanatory nature of many AI models reduces operators’ trust.
The integration of AI with IoT in solar power systems allows real-time, adaptive control and fault detection. This approach improves system reliability and efficiency. Nevertheless, major challenges such as interoperability issues, cybersecurity risks, and the opaque nature of AI decision-making have stalled the full-scale deployment and operator confidence in such methods.
9. Challenges and Limitations of AI in Solar Energy
In spite of AI being often portrayed as very promising, there are various studies that have shown AI applications are still very limited. These studies point out that a wide range of limitations exist. These challenges include not only technical issues but also systematic and operational challenges that constrain the adoption of AI in solar energy infrastructure. The main challenges regarding the limitations of applying AI to solar energy systems are:
9.1. Data Quality
The main and first issue is the quality and variability of data and real-time adaptability. For instance, the accuracy of the model may be affected because the collected data from the field are often incompatible. This issue is caused by environmental factors, sensor calibration differences, and geographical heterogeneity. In practice, this translates into algorithms that perform well in a controlled environment and yet they fail when applied to real-life conditions (Ukoba et al. 2024).
Moreover, Sambasivan et al. 2021 reveal that low data quality in machine learning systems often originates at early stages and specifically during “problem definition” or even “data collection.” Then the poor-quality data cascades through the entire pipeline. and results in costly downstream failures in model training, evaluation, and deployment.
Fig. 18. Poor data quality cascading through the machine learning pipeline, according to Sambasivan et al.,2021.
9.2. Model Interpretability
Moreover, there are also forming concerns about the interpretability of the AI models. AI techniques, specifically deep learning methods, lack transparency in their results. In other words, since these models are regarded as black boxes, it would be inherently demanding for policymakers, as well as practitioners, to understand how predictions are made. As a result, planning maintenance and real-time energy management could be more complicated when AI modeling is involved (Afridi et al. 2021).
9.3. Model Evaluation
The literarue warns about overly optimistic reported results on AI adaptability in the renewable energy sector. The heterogeneity of the dataset comes in many forms, including but not limited to the locations and sensors that were involved in the gathering of the data, the differences in time periods, weather regimens, and variations in the resolution of data. The factors create an inconsistency between the training and deployment phases and result in models that are overfit to limited conditions (Hu et al. 2022).
9.4. Deployment Risk
Deploying AI models in smart solar-powered systems with safety-critical situations can introduce technical, financial, and operational risks (Park C. 2025). For instance, an AI model trained on infrared images from one camera brand is at risk of missing hotspots when adapted to images from another brand due to resolution differences. Also, an irradiance forecaster tool trained on clear-sky-dominated data is prone to over/undershooting in case dust or smoke reduce the direct normal irradiance.
Deploying an unsuitable model in a more sensitive setting can also result in cost overruns, cybersecurity breaches, and interoperability failures. Such risks demonstrate the importance of a robust adaptation mechanism (Park C. 2025).
Despite showing a vast potential, AI applications in solar energy face critical issues such as : low data quality, lack of model interpretability, and mismatches between training and deployment environments. These challenges, when paired with deployment risks especially in safety-critical environments and conditions, necessitate the importance of transparent, robust and adaptable AI frameworks that are specifically tailored to real-world scenarios.
10. Future Horizons of Applied AI in Solar-Powered Electrical Systems
Below are key areas where AI applications are most probably going toward , as to take solar power systems beyond their current levels:
- Maximum Power Point Tracking (MPPT): AI-based MPPT techniques will improve the efficiency of solar power systems by adjusting operating points dynamically in order to derive maximum power from solar modules. Such techniques have shown better efficiencies compared to other traditional MPPT techniques in complex and rapidly changing environmental conditions (Yap et al., 2020).
- Integration of IoT with Machine Learning: In modern concepts, the performance of smart solar energy management systems is improved by applying IoT solutions and machine learning algorithms. IoT sensors collect real-time data on several system parameters, and advanced machine learning models optimize energy generation, consumption, storage, and distribution together while detecting anomalies in systems (Ismail et al., 2025).
- Solar Energy Production Forecasting: Applied machine learning models are very useful for defining strategies related to sustainable energy planning and enhancing total efficiency in solar power systems (Liu, 2024).
- Design, Forecasting, and Maintenance of the Photovoltaic System: AI techniques can overcome the high computational costs and huge volume of data associated with design, forecasting, and maintenance of photovoltaic systems and provide automatic intelligent history-based solutions (Kumar et al., 2024).
- Hybrid Power System Optimization: AI-hinged methodologies can be used to analyze and optimize hybrid power systems to achieve the best performance, energy efficiency, and financial viability, as shown by Soni et al. 2024, who executed a multi-objective objective on a grid-tied solar PV-fuel cell system.
In summary, the use of AI-based techniques in the near future is expected to enhance efficiency and optimization, leading to improved decision-making in all areas of solar power generation and management. Combining AI-based methods with the solar power system will help create energy solutions that are good for the environment and last a long time.
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11. Conclusion
Artificial intelligence is a mighty force in reshaping not only the design but also the operation and management of solar-powered systems. AI transforms former relation limits of variability and uncertainty that once defined renewable power in a wide range of applications, from forecasting irradiance down to real-time energy dispatch.
Machine learning and deep learning models are not just analytical but adaptive decision-makers that learn from variant environmental conditions and approximate the future demand while optimizing output power and final cost in ways impossible for conventional techniques. Integration of AI across most subfields of solar energy serves a common end: converting raw data to operational intelligence and moving toward solar energy systems that are efficient, resilient, and self-sustaining.
However, issues such as data quality, interpretability, interoperability, and deployment risk continue to challenge this journey towards full autonomy in solar power. Literature points out that the solution can be found by putting focus on explainable AI frameworks, standardized datasets, and increased connectivity between academia, industry practices, and policy development.
Such an outcome will arguably mean that in the near future, AI, IoT, and edge computing may continue to take solar infrastructure all the way into making it an ecosystem of intelligent, distributed systems and sustainable assets, with the building blocks for a genuinely adaptive carbon-neutral energy future falling into place. The future of AI in solar energy systems lies in developing fully autonomous control networks capable of maximizing efficiency and ensuring long-term sustainability.
Author: Hooman Aminzadeh Vahedi
M.Sc. in Renewable Energy Engineering, University of Tehran
Email: hoomanaminzadeh.pegahgroup.com@gmail.com