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AI Use Cases

A collection of over 250 uses for artificial intelligence

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Predicts renewable energy output from wind and solar

Introduction

The energy sector has played a crucial role in the global economy even since the earliest times. In the recent past, the concentration has been on the renewable energy sources like wind and solar energy. However, the output from these sources is affected by many factors such as weather, geographical location, and time of the day making it very hard to estimate. This has changed with the use of advanced technologies such as machine learning (ML) and artificial intelligence (AI). ML AI can process large amount of data at the real time for the energy output forecasting thus improving the efficiency and reliability of the grid.

Challenges

The process of the energy industry’s shift to the use of renewable energy sources is not without its difficulties. One of the major challenges is the intermittent and inconsistent nature of wind and solar resources. This is where accurate forecasting plays a vital role in meeting the supply and demand gap, but more often than not, conventional methods fail. Another issue is that the management and optimization of wind turbines and solar panels is complicated due to the fact that they are installed in harsh environments. These conditions’ effects on energy production are crucial to know for preventive maintenance as well as to extend equipment’s life. There is also a problem of vast amounts of data produced by renewable energy systems and how to store, analyze and use this data in real time.

AI Solutions

There are several challenges that currently exist in the energy sector and AI and ML offer innovative solutions to these challenges. ML models can use historical and real time data from various sources to give a close estimate of the energy output. These sources include weather predictions, data gathered from sensors in the equipment and geographical information. For example, it is possible for AI to determine the wind speed and direction and thus place wind turbines in the right location. Likewise, it can also predict the amount of sunlight that will shine through during the day and pivot the position of the solar panels to capture the most light. It also helps in identifying the trends in equipment data to know when certain parts might fail and the need for maintenance. There are aspects like big data and complexity that are involved in these tasks and that is why technologies such as neural networks and deep learning can be useful. There are many companies across the globe that are using AI to change the face of the renewable energy sector and some of them include IBM and Google.

Benefits

The benefits of using ML AI in the energy industry are manifold. Improved forecasting accuracy can significantly enhance grid reliability and efficiency. It enables energy providers to manage supply and demand effectively, reducing the need for energy storage or backup power plants. Predictive maintenance can reduce equipment downtime and extend its lifespan, thereby lowering operational costs. It also promotes safety by preemptively identifying potential equipment failures. Moreover, the ability to make real-time decisions based on data analysis allows for rapid response to changes in energy availability or demand.

Return on Investment

It is possible to achieve a high ROI when implementing ML AI in the energy sector. Although, there are many challenges that include the high costs of purchasing and integrating such technologies, the benefits that come with efficient and effective implementation of these technologies such as improved efficiency, reduced costs, and longer equipment life can be substantial. For instance, the National Renewable Energy Laboratory (NREL) in the United States of America stated that enhanced wind power prediction as a way of optimizing the utilization of wind turbines led to the reduction of operational costs by a minimum of $1 million for some wind farms annually. Also, the increasing focus on renewable energy generation, and the need for increased efficiency and effectiveness in the energy sector are expected to enhance the use of artificial intelligence and machine learning in the sector and offer high returns on investment in the future.

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