AI powers next phase of digital energy transformation

Artificial intelligence has become a key enabler of an irreversible shift from conventional centralized energy systems toward digital, intelligent, and interconnected infrastructures, according to a new review published in the journal Energies that sheds light on how AI is reshaping digital energy systems and renewable energy infrastructures across the world.

The study titled Review of Artificial Intelligence Applications in the Digital Energy and Renewable Energy Infrastructures presents an up-to-date systematic assessment of how AI technologies are being deployed to manage complex power systems, optimize renewable integration, and support the transition toward a more sustainable and decentralized energy future.

AI at the core of energy digitalization

According to the study, digitalization, decentralization, and decarbonization are deeply interconnected processes. As renewable energy sources such as solar and wind expand, their variable and stochastic nature introduces new challenges for grid stability and demand balancing. Artificial intelligence is emerging as the tool capable of managing this complexity.

The authors identify five key functional areas where AI applications are actively transforming the energy sector.

• Forecasting electricity generation from renewable energy sources: Because renewable output depends on weather conditions and other fluctuating environmental factors, accurate forecasting is essential for grid reliability. AI-driven predictive models analyze historical generation data, meteorological inputs, and real-time sensor information to anticipate production patterns with greater precision than traditional statistical models.
• Forecasting demand and electricity price fluctuations in spot markets: As markets become more dynamic and interconnected, machine learning systems are increasingly used to predict consumption trends and price volatility. These tools support both grid operators and energy traders in managing risk and optimizing resource allocation.
• Real-time management of energy flows and assets in active microgrids: Decentralized energy systems require continuous monitoring and adaptive control. AI-powered algorithms process sensor data from distributed energy resources, storage systems, and consumption points, enabling rapid adjustments that maintain balance between supply and demand.
• Advanced data processing and analytics: Modern energy systems generate massive volumes of data from smart meters, industrial equipment, grid sensors, and Internet of Things devices. AI tools transform this data into actionable insights, supporting predictive maintenance, asset optimization, and operational efficiency.
• Broader industrial applications: AI supports load management, system optimization, and intelligent decision-making across complex energy infrastructures.

Collectively, these domains demonstrate that AI is not limited to isolated technical tasks. It is embedded across the full spectrum of energy production, distribution, storage, and consumption.

A structured roadmap for AI adoption

The study proposes a roadmap for AI adoption in energy systems. Rather than treating digitalization as a spontaneous innovation process, the authors outline a structured, three-phase approach designed to guide countries and organizations through AI integration.

The first phase focuses on strategic planning. This stage includes defining goals, assessing existing IT infrastructure, evaluating available data resources, conducting labor market research for qualified personnel, setting budgets, forecasting return on investment, and ensuring compliance with regulatory frameworks. The authors stress that without clear objectives and infrastructure readiness, AI initiatives risk fragmentation and inefficiency.

The second phase centers on infrastructure implementation and pilot deployment. During this stage, organizations invest in computing power, provide access to AI models, launch national or institutional AI literacy programs, and implement targeted pilot projects that produce measurable results. Training personnel and identifying routine processes suitable for automation are critical steps in this phase.

The third phase involves scaling, monitoring, and long-term governance. Successful pilot projects are expanded, technical documentation is formalized, deployed solutions are maintained and audited, and continuous monitoring systems are established. The roadmap emphasizes the creation of technology hubs that oversee adoption phases, attract talent, and track global technological developments.

This structured approach underscores that AI integration is not purely technical. It requires coordinated investment in infrastructure, workforce development, regulatory compliance, and institutional capacity.

Building an AI-enabled energy democracy

The study introduces a conceptual framework referred to as AI-enabled energy democracy. This model reflects the shift toward decentralized energy systems in which communities, distributed generation units, and consumers play a more active role.

To support this transformation, the authors present a four-layer structural model that integrates physical infrastructure, digital systems, information flows, and governance mechanisms. The model is designed to ensure that AI integration enhances transparency, efficiency, and participation rather than reinforcing centralized control.

The study also provides a taxonomy of the hierarchical structure of AI, clarifying the relationships between artificial intelligence, machine learning, and deep learning. By mapping these layers, the authors aim to help policymakers and engineers develop integration strategies that align technologies with specific energy sector needs.

Importantly, the review highlights the growing role of enabling technologies such as the Internet of Things, blockchain, digital twins, and edge computing. IoT devices generate real-time data from smart grids and energy assets. Blockchain can enhance transparency and security in energy transactions. Digital twins create virtual replicas of physical systems for monitoring and optimization. Edge computing allows data processing closer to the source, supporting real-time decision-making in distributed energy networks.

LLMs are also discussed as emerging tools with potential applications in energy system modeling, optimization, and decision support. Although still evolving, these models may contribute to more advanced forecasting and system analysis capabilities.

Policy context and global competition

Many countries have adopted national AI strategies that include energy as a priority sector. The authors examine examples from Canada, China, Japan, the United Kingdom, and the United States, illustrating how governments are investing in AI research, infrastructure, workforce development, and regulatory frameworks.

The study notes that AI adoption rates vary across regions, with some countries absorbing investment more rapidly and advancing innovation ecosystems. The global race for AI leadership is closely tied to economic growth and industrial competitiveness. According to cited projections, AI could contribute trillions of dollars to global economic activity by the end of the decade.

The authors acknowledge an innovation gap between leading AI nations and other regions. They argue that structured roadmaps and harmonized standards are essential to avoid fragmentation and ensure equitable progress.

Challenges and future directions

While the review focuses on AI’s transformative potential, it does not overlook significant challenges. The computational complexity of advanced algorithms, the need for robust big data processing systems, and the preprocessing of datasets for predictive modeling remain technical hurdles.

Cybersecurity emerges as a critical concern. As digitalization expands, energy infrastructures become more exposed to cyber threats. AI can strengthen security by detecting anomalies and automating threat identification, but it also introduces new vulnerabilities that must be managed through rigorous governance.

Another pressing issue is the carbon footprint and environmental impact of AI technologies themselves. High-performance computing and large-scale model training consume substantial energy resources. The authors identify the need for further research on balancing AI-driven efficiency gains with its environmental costs.

Legacy system integration also presents difficulties. Many energy infrastructures rely on older technologies that were not designed for digital interoperability. Integrating AI into these environments requires careful engineering and phased modernization strategies.