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AI for Renewables: Ways to Optimize Wind, Solar and Energy Storage

Table of Contents

The Application of AI to Renewable Energy Source Optimisation

In the future, wind, solar, biomass, geothermal, and hydropower are expected to dominate energy production. Many industrial leaders are experimenting with technology that can be used to leverage renewable sources of energy. Prediction-based technologies like Artificial Intelligence (AI) help enhance renewable energy system performance beneficially.

AI’s impact on renewable energy supply: how can it help?

AI can benefit the renewable energy sector in a variety of ways. Let’s see it in detail.

Weather forecasting

The reliability of renewable energy is highly influenced by factors like sunlight, wind, and water. These natural resources are highly dependent on the weather. The unpredictability of weather forecasting is a great challenge to optimising renewable energy sources. Here comes the significance of new-age technologies like AI and machine learning. AI can be useful in making faster and more accurate predictions. When compared to the traditional methods of forecasting, AI-based weather forecasting is a reliable approach for solar or wind forecasting.

Enhance plant efficiency

Monitoring renewable energy plant performance can be greatly facilitated by the use of artificial intelligence. An accurate forecast can be obtained with data-driven intervention, allowing power supply and demand to be more accurately predicted. With AI, observations and insights can now be gathered in real-time. By analyzing energy collection data and analyzing energy consumption data, AI-powered systems will provide insights into energy use. In this way, the overall efficiency of plants can be improved. 

Grid management

To move away from legacy infrastructure-intensive models, a grid-based AI system will be necessary. Interconnecting the energy grid with devices and sensors allows for collecting a great deal of data. As a result of this system, energy suppliers will be able to intelligently adjust supply and demand based on market conditions.Intelligent storage units can also match the flow of supplies.

Predictive maintenance

The equipment employs sensors that provide algorithms with rich datasets for predicting potential issues. Optimising problems on an industrial scale can be made possible using AI, for example, preventing wind turbine failures before they occur. It helps establish the timeframe required for regular maintenance.

Improved farm design

Designing clean energy farms can be assisted by algorithms powered by artificial intelligence. The task of designing them is usually very complex, requiring a lot of resources and longer time frames. This process is simplified and less time-consuming with AI algorithms.

Safety improvements

In addition to maintaining renewable resources’ intermittency, it is possible to improve safety, efficiency, and reliability with AI. It helps understand energy consumption patterns and identifies energy leaks. Furthermore, AI and machine learning can assist in identifying threats and preventing cyberattacks.

Conclusion

Increasing demands for renewable energy are expected in the coming years. AI-based methodologies in the renewable industry sector help improve weather prediction, asset maintenance, and overall efficiency. Moreover, using AI and machine learning can help us analyse data and make predictions of power demands. 

In short, optimising intermittent renewable resources with the help of AI techniques is the future. An MBA in Energy in Sustainability could be an excellent place to start if you’re thinking about a career in energy and sustainability. 

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