Artificial Intelligence (AI) is often discussed as a technology that could transform sectors ranging from healthcare to finance. One field that AI has already started transforming is the energy sector. AI has been rapidly adopted in the renewable energy industry to help with decision-making, cost reduction, and efficiency improvements, among other things.
The Role of AI in the Renewable Energy Industry
AI has several applications in the renewable energy industry, right from installation to operation and maintenance. Here are five ways it is transforming the sector:
1. Site Selection and Planning
A major challenge for renewable energy start-ups is identifying the most suitable site for a wind or solar farm. Applying AI in this area means companies can analyze vast tracts of terrain data and identify the characteristics of the terrain. This includes analyzing satellite images to identify factors like vegetation, slope, and topography that could impact the power output a company can expect to achieve. AI can also be used to help make predictions about the weather, which in turn, helps companies make better decisions about site selection and setup. This reduces risks associated with setting up a project in an unsuitable location, preventing wastage of resources.
Furthermore, using AI for site selection also allows the project team to run simulations and evaluate designs for different sites, gradually refining the system to achieve maximum efficiency, and reduce operating costs in the long run. AI reduces the time and personnel needed to conduct these activities as well.
AI algorithms help identify new sites for renewable energy generation by examining the use of underutilized sites like rooftops or brownfields to maximize the use of space and resources.
2. Managing Energy Flows and Integration with the Grid
Renewable energy generation, like solar and wind, often produces energy intermittently. At times, there may be excess energy generation, while at other times, there may be a shortage, or the output may need to be curtailed. This intermittency of renewable energy production poses significant challenges for grid operators. For instance, when there is a surplus of power being produced, either the companies will have to pay for it, find storage options to stop it from being wasted or feed it to the electric grid. Depending on the grid’s capacity, the operators may not be able to dynamically balance the supply and demand, which often leads to overloading or underutilization.
AI algorithms can help manage energy flows and ensure stability within the electrical grid. Such technologies can supervise and control several distributed energy resources in real-time. This enables renewable energy companies to predict when there will be energy surpluses and shortages. This data can then be used to take preemptive action, meaning any excess energy is automatically sold on the wholesale market to avoid wastage. It can also be integrated into the grid, thereby reducing the need for energy storage solutions and increasing the reliability of renewable power output.
3. Predictive Maintenance and Lifetime Extension
Renewable energy systems, whether wind turbines, solar panels or other forms of green energy, require maintenance at regular intervals. The problem is that this maintenance can sometimes be reactive, rather than proactive. This means companies order expensive replacement components to fix a problem that, with the correct data and analytics, could have been anticipated before reaching critical failure. Early diagnosis and repair are one of the most effective measures to reduce downtime and improve renewable energy equipment’s overall availability.
AI algorithms can analyze datasets from the renewable energy systems and detect anomalies before they happen. This means equipment can receive preventive maintenance before issues become serious, reducing downtime and extending the equipment lifetime. Predictive maintenance algorithms can save costs for companies by reducing the number of unplanned maintenance activities, prevent the need for replacement parts, and prevent safety issues from happening, thereby enhancing safety.
4. Enhancing Sustainability
Renewable energy production signifies a major step towards a sustainable future. However, the manufacturing of renewable energy equipment, such as wind turbines, requires several mineral resources, significant energy input, and water use. This raises environmental concerns. By employing AI algorithms, renewable energy companies can optimize a system’s environmental performance. For instance, AI can determine which combinations of alternative materials may be used to reduce the materials used and the associated environmental impact. Similarly, AI can help companies identify choices that use less water or energy for manufacturing. This helps in reducing the carbon footprint of renewable energy and positioning it in the realm of environmental sustainability.
5. Cost Reductions
The most prominent benefit of AI in renewable energy is cost savings, which play a crucial role in enhancing profitability. While renewable energy costs have fallen significantly in recent years, being able to reduce the cost of producing renewable energy further will lead to an increase in adoption rates. AI technologies help identify the most efficient renewable energy systems configurations and minimize waste, leading to cost savings. It also enables employees to focus on complex tasks while automating more straightforward work.
Examples of AI Technologies in the Renewable Energy Industry
Smart Wind Energy Systems by GE
Wind turbines are essential for wind energy production. To enhance the performance of wind turbines to achieve maximum energy output, GE developed smart wind energy systems using AI. This involves the use of algorithms to analyze data from turbine sensors. Supervisory Control And Data Acquisition (SCADA) systems integrated with AI perform the sensing and act as the decision engine, improving wind turbine performance and maximizing uptime.
Azure Kinect Sensors by Microsoft
Microsoft’s Azure Kinect and machine learning models have led to accurate weather forecasting by analyzing wind patterns, temperatures, and barometric pressures. Using these technologies, Microsoft has been able to assist renewable energy producers by enhancing the production predictability of wind turbines with up to 36 hours of advanced warning. With the AI model of the wind turbine’s operation, companies know what wind speed they need to reach to produce optimal energy output.
PVComplete
PVComplete provides solar array design tools that work in tandem with AI. With the help of AI algorithms, it has been easier for PVComplete to train computer models to tell people what’s wrong with their designs or where to place solar panels most efficiently. Once installed, PVComplete tracks every solar panel in the array and how it is working. It manages data and produces analytics on the system to provide insights into system performance. On top of being automated, this also reduces the level of maintenance required.
Enphase Energy
Enphase Energy has developed an AI-based cloud platform that leverages machine learning to enhance the performance of solar systems by analyzing system data and optimizing performance. This is done by tracking the system performance of individual solar panel micro-inverters, working to pinpoint any issues with the panels, or anything that could affect energy production.
The Role of NLP in AI for Renewable Energy
Natural Language Processing (NLP) is a critical component of Artificial Intelligence that is revolutionizing the energy sector. NLP systems allow users to interact with AI systems utilizing direct language, which is an intuitive and user-friendly solution. In recent years, several renewable energy companies have started using NLP systems to help them achieve energy production targets and improve renewable energy planning and management.
One possible application of NLP involves the use of chatbots to improve customer communications. Chatbots are automated programs that assist users by answering frequently asked questions and performing tasks, without human intervention. Renewable energy companies can use these chat-bots to provide insights on topics like energy savings tips, tariffs, frequencies, smart metering, and general energy and billing-related inquiries from customers. This reduces the workload of the employees and expedites problem resolution.
Conclusion
The renewable energy sector is undergoing significant transformations with AI technology. AI is revolutionizing the renewable energy sector by increasing efficiency, reducing costs, and improving system performance. AI algorithms are invaluable tools that are optimizing energy production and consumption. They are making energy systems more intelligent and are ultimately driving the transformation towards sustainable energy and a green revolution.
The examples listed above are only a fraction of the many ways AI is reshaping the renewable energy sector, and it is clear to see the potential for artificial intelligence to have an even greater impact on renewable energy in the coming years. These new advancements and innovations not only enhance renewable energy infrastructure but also increase public demand and acceptance of clean energy technology.
The result of AI in renewable energy is aiding environmental sustainability, creating jobs in the energy sector, and providing greater access to renewable energy across the globe. As technology continues to advance, the possibilities for AI applications within the renewable energy industry are vast, and AI will undoubtedly play an essential role in driving this technological innovation to make “green” energy more accessible, efficient, and productive.
