Prepared by

Naglaa Fathei Mohamed Fahim

PhD Researcher in Economics

Benha University

Egypt 

Abstract

This study addresses the role of AI in supporting and developing the renewable energy sector in Iraq. It explains that despite the vast potential of renewable energy resources in Iraq, the contribution of renewable energy to the total final energy consumption remains limited. The study emphasizes that integrating artificial intelligence technologies into the energy sector can be a strategic option to overcome current obstacles and accelerate the transition towards greater use of renewable energy. This approach is closely linked to the goals of the Iraqi National Artificial Intelligence Strategy (INSAIN 2026), which focuses on digital transformation in the energy sector to enhance efficiency, reduce carbon emissions, and drive the country towards achieving carbon neutrality. The study concludes that artificial intelligence represents a strategic necessity rather than merely a technical choice to propel Iraq towards a sustainable transformation in the energy sector. Achieving this requires developing digital infrastructure, enhancing data management and analysis, and building human and institutional capacities to fully activate the potential of AI in building a resilient, efficient, and sustainable energy system.

Keywords: AI – Smart Grids – Predictive Maintenance – Solar Energy – Wind Energy – Energy Efficiency.

المستخلص تستعرض هذه الدراسة تأثير الذكاء الاصطناعي في تعزيز وتطوير قطاع الطاقة المتجددة في العراق. وتشير إلى أن العراق يمتلك موارد هائلة للطاقة المتجددة، ورغم ذلك تبقى مساهمتها في إجمالي استهلاك الطاقة محدودة. تبرز الدراسة أهمية دمج الذكاء الاصطناعي كاستراتيجية ضرورية للتغلب على التحديات الحالية وتحفيز التوسع في استخدام الطاقة المتجددة. هذا المسار يتماشى مع أهداف الاستراتيجية الوطنية العراقية للذكاء الاصطناعي (INSAIN 2026)، التي تولي اهتماماً خاصاً للتحول الرقمي في قطاع الطاقة من أجل تحسين الكفاءة، والحد من انبعاثات الكربون، والتوجه نحو تحقيق الحياد الكربوني. توصلت الدراسة إلى أن تطبيق تقنيات الذكاء الاصطناعي ليس فقط خطوة تقنية، بل ضرورة استراتيجية لدفع العراق نحو منظومة طاقة مستدامة. ولتحقيق هذه الغاية، يجب التركيز على تطوير البنية التحتية الرقمية، وتعزيز إدارة البيانات وتحليلها، بالإضافة إلى الاستثمار في بناء الكفاءات البشرية والمؤسسية لضمان الاستخدام الفعّال لهذه التقنيات في إنشاء نظام طاقة مرن وفعال ومستدام.

Introduction:

Since power grids are among the most complex systems requiring real-time decision-making, artificial intelligence technologies stand out as practical and effective solutions. On this basis, integrating artificial intelligence into energy management and the development of maintenance and operation processes for renewable energy systems is essential to achieve the goal of reaching carbon neutrality by 2050, in line with the principles set forth in the 2015 Paris Agreement and internationally agreed upon (United Nations, Paris Agreement, 2015). In this context, artificial intelligence technologies significantly contribute to enhancing forecast accuracy, improving operational efficiency, reducing costs, and lowering emissions, making them a key element in improving the performance of the energy sector.

Iraq’s experience is a clear example of the challenges faced by countries that heavily rely on fossil resources during their efforts towards transformation to renewable energy. Despite Iraq’s huge reserves of oil and gas, heavy dependence on these resources has led to imbalances in the economy and the environment, reducing its ability to achieve long-term stability in the energy sector. As a result, the shift towards renewable energy has become a strategic option aimed at diversifying energy sources and reducing the impact of these imbalances (IRENA, Energy Transition Assessment Iraq Report, 2025). However, the success of this path remains contingent on establishing a strong technical and institutional framework that ensures efficient management of the transformation.

In this context, integrating artificial intelligence technologies into Iraq’s energy system constitutes a fundamental element to maximize the potential benefits of renewable energy resources. This contributes to improving production efficiency, enhancing the reliability of electrical grids, and supporting decision-making related to operation and maintenance. Also, the use of artificial intelligence provides greater flexibility in facing challenges such as securing supplies, rising demand, and resource constraints, in addition to improving the economic efficiency of the sector. To ensure the achievement of this goal, adopting an integrated approach that combines the expansion of renewable energy use with the application of artificial intelligence technologies becomes an urgent necessity to strengthen the sustainable energy transformation path in Iraq, enabling it to confront crises and achieve comprehensive and sustainable development.

To achieve this, according to (Regulations AI, 2026), the Iraqi National Artificial Intelligence Strategy (INSAIN) was announced during the first meeting of the Supreme Committee for Artificial Intelligence in August 2024, representing an innovative framework aimed at adopting and developing artificial intelligence technologies within Iraq. It focuses on supporting the diversification of the Iraqi economy and reducing dependence on the oil sector, in addition to integrating AI applications in vital fields such as education and government services. This initiative reflects Iraq’s recognition of the pivotal role that artificial intelligence plays in achieving sustainable development and driving economic transformation.

Despite the economic and political challenges facing Iraq, as well as institutional and regulatory obstacles, such as the lack of comprehensive regulations governing the use of artificial intelligence, along with limited governance frameworks and weak digital infrastructure, the awareness of official and concerned bodies about the importance of artificial intelligence has driven them to call for the necessity of designing effective national legislation aimed at regulating the development and application of AI technologies.

Hence, the launch of the national artificial intelligence strategy in 2024 is an important indicator of Iraq’s readiness to gradually and flexibly overcome all these obstacles. However, this success depends on the continued pursuit of effective and implementable steps, along with strengthening the necessary institutional and regulatory frameworks to support these future efforts.

Despite the potential of renewable energy in Iraq, the heavy reliance on oil resources, alongside weak institutional and technical infrastructure, in addition to economic and political challenges, constitutes a major obstacle to achieving a sustainable transformation in the energy sector. Thus, the importance of studying how to employ artificial intelligence as an effective tool to support and develop the renewable energy system, enhancing its efficiency and sustainability while reducing the barriers facing this sector, becomes evident. Accordingly, the main problem of the study revolves around the following question: How can the employment of artificial intelligence technologies contribute to improving the efficiency and performance of renewable energy systems in Iraq, supporting the achievement of a sustainable transformation in the energy sector, especially in light of the existing economic, political, and institutional challenges? From this question arise the following sub-questions:

1. What is the current state of the renewable energy sector in Iraq, and what are the main challenges hindering its development?
2. What are the most important artificial intelligence applications that can be used in the management and operation of renewable energy systems?
3. To what extent can artificial intelligence technologies contribute to improving production efficiency, stabilizing electrical grids, and reducing costs in the renewable energy sector in Iraq?
4. What is the role of artificial intelligence in supporting forecasting, maintenance, and decision-making processes in the renewable energy system?
5. How can Iraq’s national artificial intelligence strategy support the employment of artificial intelligence in the renewable energy sector?

Accordingly, the importance of this study lies in its focus on highlighting the role of artificial intelligence as a vital and important element in achieving sustainable transformation in the renewable energy sector in Iraq. Therefore, this research relies on a methodology that combines analytical, descriptive, and deductive approaches to study the role of artificial intelligence in supporting and developing the renewable energy system in Iraq. The analytical approach is used to explore the relationship between artificial intelligence technologies and their contribution to achieving a sustainable transformation in the energy sector.

The descriptive approach is used to describe the reality of the energy sector in Iraq, while highlighting the expected impact of the Iraqi National Artificial Intelligence Strategy (INSAIN) on the development of this sector. The deductive approach is relied upon to draw conclusions and provide practical, applicable recommendations, enabling a precise presentation of how artificial intelligence can be effectively employed to enhance the renewable energy system in Iraq.

The academic importance of this study stems from its treatment of a modern and interdisciplinary topic that combines artificial intelligence, renewable energy, and sustainable development fields that are gaining increasing attention in contemporary economic literature, especially in light of global trends toward achieving carbon neutrality and transitioning to low-emission energy systems. The study contributes to filling an existing knowledge gap in both Arabic and foreign literature, particularly those related to the applications of artificial intelligence in the renewable energy sector in rentier and developing countries, and specifically the Iraqi case, which has not received sufficient specialized academic research in this field.

This study enriches the theoretical framework concerning the relationship between artificial intelligence and sustainable transformation in the energy sector by presenting an analytical model linking technological development, energy system efficiency, and sustainable development requirements within a complex economic and political context such as Iraq. It also provides a scientific reference that can be built upon in future comparative studies, whether at the level of regional countries or countries with similar economic and institutional characteristics.

Moreover, the study’s reliance on a multi-method analytical approach, including analytical, descriptive, and deductive methods, enhances its academic value, as it allows for a more comprehensive understanding.

 1-Literature Review

According to (IRENA, 2019), artificial intelligence has become one of the fundamental pillars of contemporary life, with its applications and growing uses across various sectors. Although the term artificial intelligence was first introduced in 1956, the rapid development in recent years has contributed to maximizing its role, especially in enhancing the efficiency and sustainability of energy systems at the global level. Despite the absence of a comprehensive and definitive definition, artificial intelligence is viewed as a branch of computer science aimed at developing systems that mimic human capabilities in learning, analysis, and decision-making.

These systems rely on continuous adaptation to the data they collect and analyze, allowing them to modify their behavior autonomously without the need for direct programming for each case. Artificial intelligence is based on advanced algorithms capable of recognizing patterns, drawing conclusions, and supporting decision-making processes, distinguished by their ability to handle new and unfamiliar tasks without direct human intervention. Although the terms artificial intelligence and machine learning are widely used interchangeably, machine learning is considered a branch of artificial intelligence, focusing on enabling machines to learn autonomously from data and predict future outcomes. Other technologies also fall within the artificial intelligence system, such as natural language processing, deep learning, and neural networks, due to their pivotal role in enhancing the efficiency of intelligent systems and improving their performance.

(Abdelkader, 2025) believed that artificial intelligence is a multidisciplinary field that combines computer science, statistics, mathematics, engineering, and cognitive sciences, aiming to create intelligent systems capable of learning from experiences and performing complex tasks with high efficiency. Deep learning represents one of the most prominent and advanced areas of artificial intelligence, relying on large-scale neural networks and advanced computational architectures. Practical applications of artificial intelligence have proven to have a tangible impact on improving energy consumption efficiency by reducing consumption rates and enhancing the efficiency of data centers and service systems.

According to (Majeed, 2025), artificial intelligence plays a vital role in managing and distributing renewable energy through the development of systems based on advanced technologies such as machine learning, neural networks, and inference algorithms. These systems contribute to improving energy management efficiency by predicting renewable energy production rates, regulating its flow in electrical grids, identifying peak periods, and enhancing distribution and storage processes, thereby reducing losses and strengthening grid stability. Furthermore, artificial intelligence supports the ability of smart grids to adapt to sudden changes in production and consumption, achieving a more precise balance between supply and demand and sustainably improving operational efficiency.

From the perspective of (Manuel et al., 2024), artificial intelligence plays a fundamental role in the development of the renewable energy sector, thanks to its significant impact on improving operational efficiency and enhancing the performance of energy systems, which contributes to the transition towards more sustainable practices. Predictive maintenance is considered one of the most prominent technical applications in this field, as it relies on machine learning algorithms to analyze equipment data and identify early indicators of potential faults. This approach helps enable timely preventive intervention, which leads to reducing unplanned downtime, lowering maintenance costs, and increasing the operational reliability of renewable energy systems.

In the same context, (Nelson et al., 2025) confirmed that global applications of artificial intelligence in the renewable energy sector have proven effective in improving the forecasting of solar and wind energy production, allowing for better integration of these sources into markets and electrical grids. Artificial intelligence also plays a fundamental role in dynamically managing electrical loads through real-time monitoring of supply and demand, and making autonomous decisions for energy distribution and reducing outages. Additionally, predictive maintenance is one of the most important advantages of artificial intelligence, as it enables early detection of potential faults based on sensor data analysis, which reduces operational costs and enhances the reliability of renewable energy systems.

In the Iraqi context, (Khaleel, 2025) indicated that artificial intelligence is considered a promising tool for addressing water-related challenges, as it contributes to improving water resource management, developing irrigation systems, predicting drought waves, and monitoring leaks in distribution networks. These solutions are based on technologies such as machine learning, predictive analytics, and remote sensing, enabling Iraq to use its water resources more efficiently and reduce waste. Additionally, artificial intelligence supports decision-makers in formulating data-driven strategies to ensure sustainability in the exploitation of water resources.

Also, (Aldarraji et al., 2024) emphasized the great importance of artificial intelligence in improving the ability to predict energy demand and supply in Iraq, which contributes to achieving more efficient electricity management and addressing challenges resulting from population growth and technological development. Especially since recent studies in this field rely on advanced predictive models to provide accurate forecasts regarding demand and supply levels for specific time periods. The results confirmed the importance of accurate forecasting to ensure energy security, improve resource allocation, support decision-making processes, and formulate energy-related policies.

In the study of (Majnoon & Saifoddin, 2025), it was confirmed that artificial intelligence has a remarkable ability to improve energy consumption management in urban areas, as it was able to accurately predict energy demand and contributed to supporting the decision-making process to achieve greater efficiency and more advanced sustainability. This methodology was practically tested in the city of Tehran, where the results demonstrated the potential of artificial intelligence in enhancing urban energy planning and improving the sustainability of energy systems.

2-Research Gap

Despite the continuous increase in research related to the role of artificial intelligence in the energy sector globally, a review of previous literature revealed significant research gaps, both in terms of subject matter and location. These gaps are represented in the following points:

1-The scarcity of applied studies in transitional environments, such as the Iraqi case, where most previous research focused on developed countries with integrated digital infrastructure. However, research lacks in-depth studies exploring how AI technologies can be applied in an environment suffering from a rentier economy alongside technical and institutional complexities like those found in Iraq, especially with the heavy reliance on fossil fuels.

2-The lack of studies linking Iraqi national strategies, such as the “National Artificial Intelligence Strategy 2024,” with the actual needs of the energy sector. There is an urgent need to analyze the extent to which this strategy aligns with the requirements of renewable energy, and how strategic principles can be transformed into operational mechanisms that address challenges of power outages and supply instability.

3-The absence of integrated models between predictive maintenance and water resource management. Despite the clear focus of some Iraqi studies on water and others on forecasting energy demand, there is a shortage of research presenting a comprehensive model combining predictive maintenance for renewable energy systems and smart grid management in facing harsh environmental and climatic challenges such as dust storms and drought, conditions that particularly characterize the Iraqi environment.

Based on these gaps, this study aims to address this deficiency by providing an analytical vision that combines the technical capabilities of artificial intelligence with the requirements of the Iraqi reality. Thus, the study presents a roadmap that supports the energy transition process towards a sustainable and efficient model.

3-Methodology

1-4 Importance of Artificial Intelligence in Energy Systems in Iraq

Iraq enjoys favorable conditions for the development of renewable energy sources in general, with a special focus on solar energy. In many areas, especially in the southwest, the average global solar radiation reaches about 5.8-5.9 kW/m² per day. And hydropower is considered the most important source of renewable energy in the country, contributing about 92% of the total renewable electricity production. Iraq relies heavily on the Tigris and Euphrates rivers, which provide the largest share of its water needs (Advanced Energy Technologies, 2024).

Also, climate data indicate, according to (Advanced Energy Technologies, 2024), that Iraq has promising opportunities for wind energy generation, as the average daily wind speed at a height of 10 meters above the ground over a period of ten years shows rates suitable for efficiently operating wind turbines. These speeds constitute a critical factor in assessing the technical feasibility of wind energy projects, as the increase and stability of wind speed are linked to the increased production capacity of generated electrical energy.

By leveraging these potentials, Iraq can benefit from artificial intelligence technologies in developing projects in areas with strong and stable winds, which contributes to enhancing the use of renewable energy, reducing reliance on traditional energy resources, and supporting the achievement of sustainability and clean energy goals in the long term.

According to the International Energy Agency (IEA, 2025), Iraq seeks in the medium and long term to enhance the capacities for generating solar and wind energy and integrate them with the electrical grid. This is alongside studying opportunities to develop hydropower projects. The International Energy Agency expects that the renewable energy capacity in Iraq will reach more than 2 gigawatts by 2030, representing about 4-5% of the total system capacity.

Figure (1) illustrates the evolution of the reliance ratio on renewable energy sources, which include solar energy, wind energy, hydropower, geothermal energy, as well as modern biomass, starting from the 1990s up to the year 2022. The figure reflects that in the years preceding 2011, this percentage maintained relatively low rates, with slight growth reflecting the limited reliance on renewable energy sources as part of the energy mix. However, starting from 2011, this trend witnessed a significant shift as the share of renewable energy increased at a faster pace, rising from less than 10% to more than 13% by 2022.

This development reflects the growing efforts towards energy transition and the increasing interest in expanding the use of clean energy sources, in response to environmental and economic pressures, alongside the adoption of supportive policies for renewable energy.

Nevertheless, despite the importance of this improvement, the contribution of modern renewable energy still remains below the level that could bring about a radical change in the global energy structure. This underscores the need to leverage modern technologies and tools such as artificial intelligence to enhance the efficiency of integrating renewable energy and to accelerate the transition towards a sustainable energy system.

https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=IRAQ&fuel=Renewables%20and%20waste&indicator=SDG72modern.

For clarification, Figure (2) shows the evolution of the share of renewable energy in the final energy consumption in Iraq from 1990 to 2022, which is a broader and more comprehensive perspective for measuring national progress in increasing the share of clean energy. The indicator is not limited to modern renewable energy only but links national performance to international standards, allowing for a more accurate assessment of progress. This analysis is particularly useful for highlighting the importance of applying artificial intelligence technologies in the Iraqi energy sector, where AI can be used to enhance resource management, improve the efficiency of renewable energy production, and support strategic decision-making, contributing to achieving sustainable development goals and maximizing the impact of the energy transition.

Figure (2) illustrates the development of the share of renewable energy in the final energy consumption in Iraq during the period between 1990 and 2022. It recorded low percentages before 2005, ranging between 0.3% and 1%. In 2005 and 2006, a sudden jump to about 2.5% was observed, but it quickly declined gradually and stabilized at a level below 1% after 2015, reflecting the limited sustainable spread of renewable energy.

https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=IRAQ&fuel=Renewables%20and%20waste&indicator=SDG72.

Also, Figure (3) illustrates the distribution of energy consumption in Iraq by source as a percentage of total primary energy during the period between 2000 and 2024. Oil remains the main energy source, with its share ranging between 70% and 90%, with a slight decline in its share after 2015. In contrast, natural gas has witnessed significant growth, with its share rising from about 10% to around 30% by 2020, reflecting a trend towards diversifying energy sources. Conversely, the contribution of renewable energy and hydropower remains very limited, usually not exceeding 5%, while coal and nuclear energy are almost entirely absent from the national energy mix. Therefore, these indicators point to the importance of doubling efforts to enhance the integration of renewable energy into the national system. This requires leveraging artificial intelligence technologies to improve the management of electrical grids, forecast production and demand, and increase the efficiency of integrating renewable sources in order to achieve a sustainable transformation in Iraq’s energy sector.

https://ourworldindata.org/grapher/share-energy-source-sub?time=2000..latest&country=~IRQ.

And Figure (4) shows the fundamental developments in the electricity production structure in Iraq during the period from 2000 to 2024, where reliance remains almost entirely on fossil fuels, with a clear change in the roles of oil and gas. The share of natural gas rose from less than 20% in 2000 to about 60% by 2024, while the share of oil decreased from more than 80% in 2004 to around 40%, while the contribution of hydroelectric power remained almost modest. This heavy reliance on traditional energy sources and the fluctuating production ratios between oil and gas highlight the urgent need for innovative technologies such as artificial intelligence, which can improve resource management and electricity distribution more efficiently, predict network loads, and maximize the utilization of limited renewable sources. Artificial intelligence can also support the energy transition in Iraq through preventive maintenance of power plants, improving the operational performance of electrical networks, and reducing losses, thereby enhancing stability and creating a more conducive environment for expanding the use of renewable energy in the long term.

 https://ourworldindata.org/grapher/share-elec-by-source?time=2000..latest&country=~IRQ.

To understand the role of artificial intelligence in improving renewable energy systems, (Gbadamosi et al., 2025) addressed three fundamental theories: systems theory, optimization theory, and technological determinism theory. Each theory offers a unique perspective on how artificial intelligence can be employed to enhance efficiency and support sustainability.

• Systems theory: Focuses on the interaction between different components of the system, where elements of renewable energy systems such as smart grids, wind turbines, and solar panels operate in an integrated manner to ensure stability and operational efficiency. Here, artificial intelligence plays the role of an intelligent element responsible for processing data instantly to predict fluctuations in production, optimize energy distribution, and increase network flexibility.
• Optimization theory: Seeks to identify the best operational solutions while considering existing constraints. Through artificial intelligence, modern applications demonstrate their efficiency in improving smart grid management, developing energy storage systems, and directing operational processes for solar and wind power plants. This, in turn, leads to enhanced operational performance, reduced losses, and improved resource distribution.
• Technological determinism theory: Views technological developments as the primary factor reshaping societies and economies. In the energy sector, artificial intelligence emerges as a force to change how resources are managed through production forecasting, smart grid automation, and implementing predictive maintenance. All of this contributes to making more informed decisions and helps transition towards more sustainable and resilient energy systems.

2-4 Mechanisms for Integrating and Applying Artificial Intelligence in the Operation and Maintenance Processes of Renewable Energy in Iraq Despite the political and geopolitical challenges, in addition to the legislative and institutional obstacles hindering the development of the energy sector in Iraq, and with limited technical infrastructure and weak network efficiency, Iraq possesses promising and untapped opportunities in the field of renewable energy, specifically in solar and wind energy resources. The challenge lies not only in the abundance of natural resources but also in how to manage and operate them effectively within a complex institutional and technical environment.

Here, artificial intelligence emerges as a key tool capable of overcoming many structural constraints by improving the operational efficiency of energy systems, enhancing network reliability, reducing losses, and supporting data-driven decision-making. The contribution of artificial intelligence to advancing the transition to renewable energy in Iraq is manifested through its role in improving the performance of renewable energy systems, efficiently managing smart grids, applying predictive maintenance, increasing energy storage efficiency, and promoting optimal energy use.

• AI-Driven Efficiency of Renewable Energy Systems Operation Big data plays a vital role in energy management by collecting, processing, and analyzing vast amounts of information to extract clear patterns and operational insights. This data is primarily derived from sensors, smart meters, and other sources, undergoing purification and adjustment processes aimed at enhancing its accuracy and reliability, such as removing outliers and handling missing values. In this context, the Internet of Things and artificial intelligence represent a fundamental axis by enabling real-time monitoring and control tools and linking devices to each other, contributing to improved network stability and resource efficiency. This integration between big data and the Internet of Things allows artificial intelligence to play an active role in improving the operation of renewable energy systems, through predicting production and demand changes, supporting operational decisions, alongside developing innovative solutions to enhance efficiency and reduce emissions. These joint efforts contribute to achieving the sustainability of smart grids and supporting the transition towards clean and sustainable energy (Ejiyi et al., 2025).

According to (Gbadamosi et al., 2025), renewable energy systems, such as solar farms, wind farms, and hydroelectric plants, operate as part of an interconnected grid that requires high coordination to maintain balance and achieve optimized performance. In this context, the role of artificial intelligence stands out as a fundamental element, relying on intelligent control technologies to process real-time and historical data in order to predict production fluctuations, improve energy distribution, enhance storage efficiency, reduce waste, and support grid stability. Additionally, artificial intelligence helps manage loads more efficiently, analyze consumption patterns, and implement predictive maintenance to ensure optimal operation of smart grids. Through its capabilities in continuous monitoring and data-driven decision-making, artificial intelligence contributes to enhancing the systems’ ability to adapt to climate changes and unexpected conditions, making it the essential solution for achieving long-term sustainability and increasing operational efficiency for renewable energy systems.

Also, AI supports improving the operational efficiency of renewable energy systems through its ability to accurately predict fluctuations in production caused by the variable nature of energy sources such as the sun and wind. This prediction helps reduce waste and ensures an immediate balance between supply and demand, enabling the use of produced energy with the highest efficiency. Artificial intelligence also relies on hybrid models that combine multiple techniques, such as neural networks and genetic algorithms, which enhance system performance and significantly reduce errors in load management. Additionally, artificial intelligence contributes to improving the management of urban smart grids by directing renewable energy to areas that need it most at the right time, preventing overload conditions on the grid. It also plays a role in optimizing the charging and discharging cycles of batteries connected to renewable systems to ensure energy availability when the main source is absent, whether during the night or periods of low wind production. Furthermore, its role includes reducing operational costs and enhancing sustainability through predictive maintenance that detects potential faults early, allowing them to be addressed before causing system downtime (Majnoon & Saifoddin, 2025).

Therefore, AI enables renewable energy systems to operate more effectively, enhancing grid stability and optimizing resource utilization. It also helps reduce dependence on traditional energy sources, supporting sustainability and increasing the efficiency and reliability of energy systems, whether in urban or industrial environments.

Similarly, AI contributes to improving energy consumption efficiency within buildings and cities, through smart systems and devices characterized by the ability to measure, predict, and control heating and cooling systems based on actual needs. In the field of renewable energy, artificial intelligence is utilized to analyze environmental data and monitor the performance of components such as wind farms and solar power plants, with the aim of improving productivity and enhancing the efficiency of operational processes. On a broader scale, AI plays an important role in managing energy demand in cities by integrating data extracted from smart meters and Internet of Things devices, and creating real-time information systems that support better use of resources such as energy and water, helping to enhance the sustainability of infrastructure (Waheeb, 2023).

Moreover, AI contributes to improving the safety and effectiveness of energy networks, as it monitors operations related to generation, transmission, and consumption in real time, while providing proactive solutions aimed at reducing faults and outages. AI capabilities are also invested in improving maintenance scheduling and managing energy storage more efficiently. In the context of industrial innovation, machine learning helps design more effective materials and systems for energy production and storage, enhancing the sector’s ability to face environmental and economic challenges and achieve long-term sustainability goals.

For example, AI technologies play an important role in improving the performance of batteries used in Powerpack and Powerwall systems, helping households and businesses increase their self-consumption rate of solar energy and reduce their dependence on the electrical grid. Additionally, predictive models based on artificial intelligence contribute to improving the efficiency of wind and solar farms by determining the optimal orientations for directing turbines and panels, thereby enhancing energy production amid changing environmental conditions. Studies show that artificial intelligence can increase solar energy productivity by about 25% by adjusting the angles of the panels based on real-time solar radiation data (Gbadamosi et al., 2025).

• AI in Energy Optimization in Renewable Energy Systems Energy efficiency improvement means a process involving a series of measures aimed at maximizing the possible benefit from available resources. This is done by reconciling energy production with changing demand patterns, along with adapting to unstable environmental challenges and ensuring the stability and quality of the produced energy. These processes hold special importance in the field of renewable energy, which heavily depends on variable climatic factors, making efficiency improvement an urgent necessity to enhance economic feasibility and reduce energy loss (Onwusinkwue et al., 2024).

The benefits of improving energy efficiency are not limited to enhancing the technical performance of renewable energy systems but also extend to strengthening the stability of electricity grids and facilitating the integration of these sources within existing infrastructures, in addition to reducing operating costs and increasing the competitiveness of clean energy projects. Considering the intermittent nature of renewable energy sources and the challenges they impose related to matching supply with demand, improving efficiency becomes an essential condition to ensure the reliability and effectiveness of these systems.

In this context, artificial intelligence plays a pivotal role through big data analysis, real-time monitoring, and forecasting production and consumption patterns, enabling smart energy management that reduces loss, limits carbon emissions, and supports the transition towards more sustainable and efficient energy systems. In the same context, Table (1) illustrates the main AI techniques in renewable energy systems.

Table (1): Main AI Techniques in Renewable Energy Systems

Technique	Keywords	Main Purpose
Machine Learning (ML)	Prediction, Data, Scheduling	Improve energy generation and distribution, reduce supply-demand mismatch
Deep Learning (DL)	Neural networks, big data, Real-time	Forecast demand, detect faults, and manage smart grids
Reinforcement Learning (RL)	Adaptation, Rewards, Decision making	Optimize battery storage, energy flow, and economic efficiency
Fuzzy Logic	Uncertainty, Adaptive control, Operational parameters	Handle variable natural resources and maximize energy output
Generative Adversarial Networks (GANs)	Data generation, Prediction, Anomaly detection	Compensate for limited data, improve forecasting accuracy, and support grid planning
Synergistic use of AI techniques	Integration (more than one technique such as ML with RL or Fuzzy Logic with DL), Flexibility, Real-time optimization	Provide comprehensive solutions for variability, improving reliability and efficiency of energy systems

Source: Ejiyi et al., 2025

Accordingly, AI supports the improvement of forecasting, control, and operational management processes for renewable energy systems such as solar energy, wind energy, and energy storage. This development has contributed to enhancing grid reliability and enabling the effective integration of intermittent sources. By using technologies such as machine learning, deep learning, fuzzy logic, and reinforcement learning, it has become possible to analyze historical and real-time data to predict energy production and demand, optimize distribution, increase storage efficiency, reduce losses, and minimize the likelihood of outages. These technologies also effectively support smart grids by enabling real-time monitoring, early fault detection, and automatic response to operational deviations, which enhances grid stability and resilience. Additionally, advanced models such as explainable artificial intelligence, graph neural networks, and physics-guided neural networks improve transparency and forecasting accuracy. Systematic reviews play a crucial role in consolidating knowledge and identifying research gaps, which in turn contributes to encouraging innovation and supporting the development of more sustainable and efficient renewable energy systems to meet future needs (Razak et al., 2025).

Within smart grids, machine learning and reinforcement learning algorithms are employed to analyze energy consumption patterns, predict peak demand periods, and distribute energy automatically and in real-time. This enhances the decentralization of energy production and significantly improves its efficiency. Additionally, artificial intelligence enables policymakers to obtain accurate insights into consumption trends, carbon emissions, and renewable energy potentials, supporting strategic planning processes and boosting investments in sustainable energy. For example, the European Union has adopted predictive models based on AI technologies to improve the deployment of renewable energy, leading to reduced reliance on fossil fuels and accelerating the transition towards a low-carbon economy (Gbadamosi et al., 2025).

• AI for Predictive Maintenance in Renewable Energy AI technologies such as deep learning, neural networks, and predictive analytics are fundamental pillars in enhancing the efficiency and reliability of renewable energy systems, as they contribute to improving energy utilization and effectively implementing predictive maintenance. Deep learning enables the analysis of complex and continuous data to detect potential faults early in wind turbines and solar panels, despite requiring significant computational resources and the complexity of interpreting its internal operations. Neural networks, on the other hand, are characterized by their ability to understand complex patterns and adapt to variables, making them an effective tool in predicting the lifespan of critical components, with their efficiency depending on the quality and volume of available data. Meanwhile, predictive analytics provides clear and understandable insights regarding future faults and better maintenance scheduling, despite facing challenges in dealing with highly complex dynamic systems. The choice of the optimal technology depends on the nature of the application, data characteristics, and operational requirements. However, integrating these technologies enhances intelligent energy management, reduces system failures, and supports the efficiency and stability of renewable energy solutions (Onwusinkwue et al., 2024).

In the field of predictive maintenance, artificial intelligence has a fundamental role, where condition monitoring systems rely on analyzing sensor data used in wind turbines and solar panels to identify abnormal conditions and predict faults before they occur. This approach contributes to reducing downtime, lowering maintenance costs, and improving the overall efficiency of systems. This accelerating trend towards the use of artificial intelligence reflects the importance of technological innovations in reshaping the methods of managing and operating energy systems, in line with the concepts of technological determinism, as modern technologies play a pivotal role in reshaping the infrastructure in the energy sector (Gbadamosi et al., 2025).

Energy consumption forecasting is a fundamental element for energy service companies, as it directly affects their decisions related to infrastructure development, supply provision, load management, and cost planning. Accurate estimation of future needs helps ensure the company is prepared for market demands and avoids energy shortages for consumers. To improve the forecasting process, the load forecasting process is divided according to the time horizon into four main categories (Aldarraji et al., 2024):

• • Long-Term Load Forecasting (LTLF): Covers periods extending to months or years, used for energy pricing evaluation and risk management.
  • Medium-Term Load Forecasting (MTLF): Includes periods ranging from days to months, assisting in analyzing the economic impacts of energy systems.
  • Short-Term Load Forecasting (STLF): Deals with periods extending from a few minutes to several days, providing precise insights into consumer behavior patterns.
  • Very Short-Term Load Forecasting (VSTLF): Covers a time frame usually not exceeding three hours, used to ensure immediate control over energy consumption.

In recent years, machine learning and deep learning techniques have become common tools to improve the accuracy of energy demand forecasting. What distinguishes deep learning models is their ability to process vast amounts of complex data using interconnected layers that produce accurate and highly reliable predictions. In contrast, traditional neural networks focus on handling static data, whereas smart grid data often comes in the form of time series affected by changing patterns reflecting past events. Therefore, recurrent neural networks (RNNs) are relied upon to process this dynamic data, characterized by their ability to retain necessary information.

• AI Applications in Renewable Energy Sources Technological developments throughout history have contributed to enhancing the efficiency of renewable energy and reducing its cost. In solar energy, crystalline silicon cells in the 1950s marked a significant breakthrough that laid the foundation for the commercial solar energy industry. Over the years, continuous improvements in material sciences and manufacturing processes have increased the efficiency of solar cells and lowered costs, making solar energy one of the fastest-growing energy sources globally. As for wind energy technology, it has undergone radical changes, with modern horizontal-axis turbines differing significantly from their traditional counterparts. Improvements in turbine design and materials have contributed to increasing their efficiency and producing larger units that generate more power, leading to reduced wind energy costs and its emergence as a major energy source. Additionally, smart grids and AI-supported monitoring systems have helped improve the efficiency of these systems and integrate them within the energy infrastructure (Algburi et al., 2025).

Nevertheless, renewable energy technologies are still continuously evolving, requiring research and innovation to address current challenges and explore new opportunities. Hence, artificial intelligence technologies in the energy sector and the shift towards renewable energy have played a pivotal role in meeting global energy demand sustainably and responsibly.

• • In modern power generation plant operations: AI significantly contributes to enhancing the efficiency and reliability of renewable energy plants by providing immediate monitoring and control solutions supported by automated analysis. AI systems rely on sensor data analysis to offer precise insights, helping predict faults and adjust the performance of solar panels and turbines according to environmental conditions and energy demand levels. This approach helps boost productivity and reduce waste. Artificial intelligence also contributes to improving the dynamic distribution of energy between generators and storage systems, alongside simulating complex operational scenarios to support informed decision-making that enhances energy management efficiency. It also works to enhance safety and reliability standards by early detection of risks, supporting the continuous production of energy in a safe and efficient manner (Manuel et al., 2024).

In brief, artificial intelligence contributes to transforming renewable energy management into an intelligent and integrated system, capable of adapting to environmental changes and improving overall performance while reducing costs. Thus, AI becomes a fundamental element in achieving a sustainable future in the energy sector.

Also, AI significantly contributes to enhancing the stability and resilience of energy supply systems by dynamically forecasting demand and optimizing the integration of renewable energy sources. These technologies help achieve an effective balance with the fluctuations in production caused by wind and solar energy. Additionally, reinforcement learning methods can be utilized to develop real-time control of microgrids, leading to increased operational efficiency and improved utilization of renewable energy sources despite variations in their output. Consequently, artificial intelligence is a transformative force in the field of renewable energy systems, enabling the analysis of vast amounts of complex data to improve prediction accuracy, regulate energy flow, and activate adaptive control strategies. These solutions rely on technologies such as machine learning, deep learning, and fuzzy logic to address various operational challenges, ranging from forecasting solar radiation and wind speeds to achieving the required balance in network demand (Razak et al., 2025).

• • In Solar Energy: AI technologies contribute to enhancing the efficiency of solar energy production by analyzing performance based on weather conditions, geographic location, and the efficiency level of solar panels. Image recognition systems and AI-supported sensors are also used for early detection of any damage or wear affecting the panels, which helps in implementing effective preventive maintenance and extends the equipment’s lifespan. Furthermore, AI assists in improving the accuracy of energy generation rate forecasts, a crucial aspect for maintaining the integrity of the electrical grid and ensuring the optimal balance between supply and demand (Shrimali & Shrimali, 2024).

Recent studies have revealed that integrating artificial intelligence into solar energy systems leads to tangible improvements in performance and efficiency. For example, in smart solar tracking systems, AI algorithms are used to dynamically adjust the angles of the panels based on the sun’s position and real-time weather conditions, increasing energy productivity by up to 20% compared to fixed-angle panels. Predictive maintenance technologies have also enabled the analysis of sensor data to detect potential issues early, such as sudden drops in output or temperature rises, contributing to a 25% reduction in downtime and a 7% increase in annual energy production. Regarding energy storage, AI-enhanced hybrid systems have shown greater effectiveness in managing battery charge and discharge cycles based on future demand forecasts, reducing energy waste by 15% and increasing grid reliability by 20%. Furthermore, accurate machine learning models have contributed to predicting solar radiation, enabling electricity companies to better balance loads and reduce reliance on traditional energy sources. This development makes solar energy more accessible and affordable, especially for rural communities and low-income groups, thereby directly contributing to enhancing energy justice (Sapre, 2024).

• • In Wind Energy: The use of AI in wind energy has led to significant changes, as it has greatly contributed to improving performance and efficiency. Predictive maintenance technologies have enabled the analysis of sensor data to detect potential mechanical faults in turbines early, resulting in reduced downtime and lower maintenance costs. Additionally, artificial intelligence has played a prominent role in optimizing the design of wind farms through modeling and analyzing turbine locations to maximize energy production based on local wind patterns. Moreover, machine learning models help predict wind pattern trends, enabling grid operators to manage fluctuations in energy production more effectively while ensuring the stability of electricity supplies.

There are studies that have revealed that artificial intelligence improves the stability and efficiency of wind energy across a range of fundamental aspects. Intelligent control technologies have contributed to adjusting the angles and blades of turbines (Pitch and Yaw Control) to maximize the utilization of kinetic energy, as well as reducing mechanical stress on turbine devices. Deep learning models have also played a prominent role in accurately predicting wind speeds, helping grid operators estimate the expected amount of energy production in advance and with precision, thereby contributing to voltage stability. Regarding predictive maintenance, since AI algorithms rely on analyzing vibration data and sounds emitted from gears and generators to detect indicators of wear or potential faults before they occur, this reduces repair costs and shortens turbine downtime, especially in offshore farms. Additionally, AI technologies contribute to managing the “wake effect” in wind farms by coordinating the operation of all turbines to achieve a more uniform airflow to the rear turbines, which increases the overall efficiency of energy production in the farm (Sapre, 2024).

• Water Systems Water plays a fundamental role in supporting renewable energy systems, whether through generating hydropower or contributing to cooling processes for solar and wind power plants, or in managing energy storage within reservoirs. Hydrological modeling also plays an important role in designing water supply networks, monitoring flood and drought conditions, and allocating irrigation water, all of which contribute to achieving sustainability and ensuring the integration of water resources with renewable energy strategies.

Amid rapid developments in the use of machine learning algorithms, such as deep neural networks and clustering techniques, it has become possible to analyze complex hydrological data with increasing accuracy. To enhance the understanding of these models and analyze their results, there is growing reliance on explainable artificial intelligence (XAI) techniques, such as the SHAP method based on game theory, which is used to determine the impact of each input factor on predictions. These tools can clarify the influence of important hydrological variables, such as rainfall amounts, temperatures, land use patterns, and soil properties, on water flows and groundwater levels. These measurements enhance the models’ ability to provide precise and straightforward explanations to support decision-making processes in a way that ensures efficient and sustainable water resource management, and consolidates the integration between water sectors and clean energy (Khaleel, 2025).

4. Conclusion The study concluded that the integration of artificial intelligence technologies in the renewable energy sector is no longer just a technical option but has become a strategic necessity to achieve energy sustainability and enhance its security, especially in countries facing structural challenges between supply and demand, such as Iraq. The results showed clear alignment with previous literature, indicating that artificial intelligence is an effective tool for addressing challenges related to the volatile nature of renewable energy. Intelligent algorithms play a pivotal role in improving the stability of electrical grids and increasing their capacity to accommodate renewable energy sources, which supports the findings of the study by (Algburi et al., 2025) regarding the contribution of artificial intelligence in tackling the complexities of grid integration and reducing reliance on fossil fuels.

Additionally, the study demonstrated that improving operational efficiency through accurate load forecasting and energy consumption management directly contributes to reducing losses and enhancing the overall efficiency of electrical grids. This aligns with what was mentioned in the study by (Majeed, 2025), which highlighted the important role of machine learning technologies in accurately forecasting solar and wind energy production, leading to a reduction in technical losses and improved performance of smart grids.

Furthermore, the study showed that adopting proactive maintenance methodologies based on data analysis significantly contributes to reducing operational costs and extending the lifespan of renewable energy components. This is consistent with the review by (Onwusinkwue et al., 2024), which emphasized the role of artificial intelligence in predicting sudden faults and ensuring uninterrupted energy supply.

In the Iraqi context, the study revealed promising potentials for developing renewable energy sources. However, there remain technical and institutional challenges that hinder optimal utilization, challenges that can be overcome by integrating artificial intelligence within the digital transformation process. This conclusion intersects with the objectives outlined in the Iraqi National Artificial Intelligence Strategy (INSAIN 2026), which focuses on digitizing the energy sector to enhance its efficiency and effectively contribute to emission reduction and achieving carbon neutrality. This also aligns with the findings of the study by (Aldarraji et al., 2024), which emphasized the importance of intelligent energy demand forecasting to address the recurring deficit in the Iraqi electrical system.

Results and Recommendations

The study reached important findings indicating the vital role of artificial intelligence in enhancing and developing the performance of the renewable energy sector in Iraq. The results highlighted that Iraq’s heavy reliance on fossil fuels and the low contribution of renewable energy to the final energy consumption reflect an imbalance in the energy system structure, necessitating the adoption of advanced technological solutions. The study found that the use of artificial intelligence technologies can play an effective role in addressing fundamental challenges, including the instability of renewable energy production, low operational efficiency, and high technical losses in electricity networks.

The results also clarified that applying artificial intelligence models for load forecasting and renewable energy production contributes to enhancing the stability of the electrical grid, improving resource utilization, and reducing dependence on traditional energy sources. The study also pointed out that predictive maintenance based on artificial intelligence technologies is an effective means to reduce operational costs and increase the lifespan of renewable energy system components, supporting the sustainability of the sector in the long term.

At the institutional infrastructure level, the results confirmed that Iraq has significant potential to develop solar and wind energy. However, weak digital infrastructure, lack of data, and limited technical capabilities pose major obstacles to fully benefiting from these potentials. Hence, the necessity to enhance digital transformation and integrate artificial intelligence technologies as an essential part of the national energy system reform strategy becomes clear.

According to the findings reached, the study emphasizes the necessity of adopting a comprehensive strategic vision aimed at integrating artificial intelligence technologies into the renewable energy sector in Iraq, in line with the objectives of the Iraqi National Artificial Intelligence Strategy (INSAIN 2026). In this context, several key recommendations were proposed:

1. The need to accelerate the implementation of the axes related to the energy sector within the national artificial intelligence strategy, by digitizing the processes of electricity production, transmission, and distribution, alongside developing unified national databases that support smart tools for forecasting and efficient network management.
2. The study stresses the importance of using artificial intelligence in forecasting energy demand and renewable energy production, due to its role in reducing the supply-demand gap and enhancing the long-term planning capacity for investments in the sector, especially with the challenges resulting from population growth and climate change.
3. Integrating predictive maintenance systems supported by artificial intelligence technologies in renewable energy plants and electrical networks is considered a key step to reduce sudden failures, limit technical losses, and improve the operational efficiency and economic sustainability of the sector.
4. The study recommended the importance of investing in human skill development by training technical and engineering teams on the practical applications of artificial intelligence in the energy field, to ensure the success of digital transformation and achieve the maximum possible benefit from modern technology.
5. The study confirmed the necessity of enhancing cooperation between government institutions, the private sector, and academic bodies to support innovation and research in the fields of artificial intelligence and renewable energy. This cooperation contributes to achieving comprehensive energy transformation in Iraq.

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