We already know that the transition to a more sustainable future relies on renewable energy as one of its key pillars. But can we optimize the production of these energy sources? Is there another technology that can help us generate more with the resources we have or improve our infrastructure?
According to the 2024 report by the International Renewable Energy Agency (IRENA), Spain’s renewable energy capacity and production reached 80,136 MW in the previous year (2023), marking a 46% increase over the past five years. This growth in renewable energy production demands efficient management of infrastructure, whether for generation (such as wind or solar farms) or for distribution and energy supply (including power grids and battery storage). In this context, Artificial Intelligence plays a key role.
How does AI help optimize renewable energy production?
Artificial Intelligence enables the anticipation of fluctuations in renewable energy production and enhances its management by focusing on two key factors: prediction and optimization.
Through advanced algorithms that combine historical data analysis, weather forecasts, real-time monitoring, and optimization models, AI determines the optimal option for each process. For example, these models can help identify the best location for wind turbines or farms to maximize energy production.
- Improves profitability: achieves greater grid stability and optimizes the balance between supply and demand, leading to higher margins.
- Increased forecast reliability: most renewable energy converted into electricity comes from solar and wind sources, both highly dependent on weather conditions. Traditional weather forecasts can be insufficient, but the combined use of different statistical models enables more reliable short-term predictions.
- Cost reduction and increased production: optimization models allow precise identification of the best locations for wind turbines or solar panels to maximize production and efficiency while minimizing costs. This ensures that the minimum number of installations needed for the desired productivity is used.
- Anomaly reduction: predictive and prescriptive maintenance leverage forecasting, optimization, and simulation models to improve intervention timing, reduce costs, and minimize losses caused by production downtime.
How Does It Work?
AI applications can be tailored to specific challenges by combining various models based on machine learning, data analysis, and advanced algorithms that use this data to determine the optimal solution.
Predictive models
In the renewable energy sector, these models leverage machine learning algorithms and numerical prediction techniques to analyze historical data on wind, solar, and hydro generation. They also consider variables such as solar radiation, wind speed, and consumption patterns to enhance forecasting accuracy.
Optimization models
These models take analysis a step further by not only processing data but also using advanced mathematical algorithms to identify the best strategies for energy generation, storage, and distribution. They support decision-making by integrating weather forecasts, energy consumption, and storage capacity, helping businesses achieve specific goals—whether minimizing costs, maximizing efficiency, or optimally balancing supply and demand.
This technology is already being used by companies to solve real-world challenges, proving that AI in the energy sector can drive significant improvements, including:
- Production optimization: an energy company faces the challenge of maximizing energy generation to increase revenue in the electricity market. AI helps optimize production strategies to achieve this goal.
- Resource optimization: when renewable energy generation is insufficient, AI-powered predictive models enable more efficient use of alternative energy sources or better storage management.
- Reduction of Operational Costs: the startup and shutdown costs of renewable energy units can be extremely high. More accurate predictions of when and how to operate these units help lower costs significantly.
- Optimal Wind Turbine Placement: an energy company needs to determine the best locations for wind turbines. AI models can identify the most efficient locations to maximize energy generation.
As in every industry, Artificial Intelligence is opening up new possibilities that once seemed far more complex. This technology, which is already available, is unlocking innovative ways to achieve more efficient energy management, tailored to the current energy landscape.
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