AI-driven simulation method boosts accuracy in solar energy potential assessment

The study responds to key barriers in the deployment of solar energy within distributed energy systems, where renewable integration often suffers from volatility and planning uncertainty. Traditional models rely heavily on historical averages and fail to account for dynamic system interactions, leading to suboptimal investment and siting decisions.

A new study introduces an artificial intelligence-driven approach to better assess the technical potential of solar energy for distributed power systems. The paper, titled “Methodology for Assessing the Technical Potential of Solar Energy Based on Artificial Intelligence Technologies and Simulation-Modeling Tools” and published in Energies, presents a hybrid method that combines AI-powered solar irradiance forecasting with advanced simulation tools to deliver more reliable, location-specific energy assessments.

The authors argue that as the global energy transition accelerates, accurate planning for solar power deployment is essential for enhancing energy security and reducing emissions. Their approach integrates predictive models with system-level simulation to help planners identify optimal sites, sizes, and configurations for solar plants in decentralized energy networks.

Integrating AI forecasting with system simulation

The study merges artificial intelligence-based forecasting with dynamic simulation modeling. The research team used historical meteorological data to train a long short-term memory (LSTM) neural network capable of predicting solar irradiance with seasonal and hourly precision. This predictive component feeds into a SimInTech simulation model, which replicates the behavior of the target energy system, including consumer loads and energy conversion equipment.

By linking forecast data with the simulated performance of a real-world energy setup, the methodology allows decision-makers to estimate the technical potential of solar energy under specific local conditions. This hybrid approach captures not only the resource availability but also how the broader system, storage, demand patterns, and conversion technologies, interacts with solar power integration.

This solution, the authors highlight, moves beyond generic solar resource maps, offering actionable insights for project planning and enabling more accurate assessments of how solar installations will contribute to energy supply and reliability.

Addressing the challenges of distributed energy systems

The study responds to key barriers in the deployment of solar energy within distributed energy systems, where renewable integration often suffers from volatility and planning uncertainty. Traditional models rely heavily on historical averages and fail to account for dynamic system interactions, leading to suboptimal investment and siting decisions.

The proposed methodology tackles these issues by:

• Improving prediction accuracy: AI-powered forecasts reflect short-term weather variability and seasonal patterns.

• Enhancing system-level insights: Simulation models evaluate how predicted solar inputs affect the overall energy balance, reliability, and equipment performance.

• Supporting siting and sizing decisions: The hybrid method offers a more precise estimation of the technical potential, reducing risks in planning new installations.

• Enabling scenario testing: Planners can model how different storage strategies, load profiles, or grid configurations affect the feasibility of solar projects.

According to the authors, this integrated assessment is crucial for designing energy systems that can make the best use of renewable resources without compromising stability or increasing costs.

Implications for future renewable integration

While the paper demonstrates the methodology in the context of a private consumer energy system, the authors suggest that it can be adapted for regional or national planning and combined with other renewable energy sources. They highlight several future priorities:

• Expanding the approach to incorporate economic and infrastructure factors for a more comprehensive analysis.
• Introducing adaptive AI algorithms to optimize not just solar generation but also energy storage and hybrid energy resources.
• Leveraging the framework to inform distributed network development strategies, ensuring that the growing penetration of solar power aligns with grid stability and decarbonization goals.

The study also points to the need for stronger collaboration between researchers, energy planners, and policymakers to translate such technical methodologies into practice. This includes aligning predictive modeling with regulatory requirements and incorporating simulation-based insights into national energy transition plans.