AI and its contribution to renewable energy projects.

Roldán Logistics
Jan 28
3 min read

Updated: Feb 4

Artificial intelligence is transforming many productive fields, and the energy sector is no exception, as is the case with renewable energy projects, solar, wind, green hydrogen, among others.

Artificial intelligence (AI) and machine learning are transforming the renewable energy sector by enabling advanced optimization and smarter decisions. These technologies are revolutionizing everything from power generation and smart grid management to improving energy efficiency. AI is redefining how we produce, distribute, and consume energy. In this article, we will explore the role of AI in renewable energy optimization and its applications across the entire energy value chain.

One of the main applications of artificial intelligence (AI) in the energy sector is the optimization of power generation. AI algorithms are able to analyze data from renewable sources, such as solar panels and wind turbines, to predict production and maximize system performance. By incorporating factors such as weather patterns and historical data, AI enables more accurate forecasts, ensuring efficient use of renewable resources. Machine learning models also help optimize operations in traditional power plants, leading to reduced fuel consumption, lower emissions, and better maintenance scheduling.

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For example, in the case of solar energy projects , the exact location for installing solar panels. Organizing the generation, distribution and consumption of energy after the incorporation of millions of private producers into the electricity grid. The transformation of the energy sector is due to the rise of solar and wind energy, which is driving a change in the interconnectivity model, where AI plays a leading role. The sources of generation are no longer just power plants, but individuals and companies that produce their own energy, driving the model towards the democratization of renewable energy sources.

Choosing the right location: the best renewable scenarios

AI is helping power companies choose the best location for wind and solar farms . The vast amount of data obtained today about the Earth's surface and environmental conditions makes it easier to select land, roofs or terraces based on climate, topography, hours of sunshine, etc. And also to cross-reference this data with the consumption needs, mobility or level of industrialization of an area.

This is because AI algorithms can adjust equipment settings in real time and make them operate at high capacity, thereby increasing energy production. In addition, optimized maintenance programs minimize downtime and maximize equipment availability.

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Predictive analytics for maintenance of renewable energy infrastructure components

AI is helping to predict the maintenance tasks required on solar panels or wind turbines. It is a paradigm shift: from standardized replacement of parts and equipment to more efficient action based on data and sensors.

For example, in Spain, the SAGA (Advanced Asset Management Solution) system has been implemented thanks to AI to better schedule the maintenance of the network infrastructure spread throughout the territory.

AI is definitely improving predictive models and collecting information in real time, avoiding the need to replace parts that can still function and extending the life of, for example, storage batteries , which are increasingly widespread in solar self-consumption. This topic offers a solution to one of the main debates, the environmental impact generated by the components of solar and wind energy projects.

In conclusion, the implementation of Artificial Intelligence enhances five key areas of wind and solar photovoltaic plants:

Design of predictive models for energy generation.
Monitoring and diagnosis of the operation.
Performance and efficiency.
Energy storage and distribution with batteries.

The transformation of the energy model in Latin American countries will lead to the implementation of Artificial Intelligence with the aim of optimizing the development of this type of projects in the region, including Colombia, a country that has potential to explore in renewable energy infrastructure such as solar and wind farms, and that in view of the growing demand, technological alternatives must be sought that reduce the risks to builders and investors.