8 Application of AI in Utility and Energy Sector

Introduction

 

Despite its pervasiveness and fanfare, not everyone knows what artificial intelligence (AI) entails. At its most basic, AI is just the ability of robots and computers to replicate human behaviour.

 

Underneath that broad term, however, lie machine learning technologies and complex AI algorithms that enable robots and computers to operate smarter and more efficiently than we ordinary mortals.

 

That is why AI's capacity to recognise patterns and abnormalities in massive data sets is such a potentially valuable tool for illness detection. 

 

Even more fascinating is the fact that AI thrives on data: As the volume of data increases, AI's capacity to interpret that mountain of data into valuable insights improves. There are multiple applications of AI in day to day life.

 

However, it is in the utility and power industries that AI is beginning to show some of its most significant effects on many sectors of the company. 

 

Power companies are increasingly relying on artificial intelligence (AI) to enhance energy supply and – in locations like the Amazon and California – to avert possible wildfires using drone management software and vegetation management.

 

AI is really improving human occupations in a post-COVID future where a smaller on-site labour is soon becoming the norm.

 

The fact that utilities and the electric sector are already researching and testing AI to help improve their operations, customer connections, and business models demonstrates how widespread it has grown.

 

(Must catch: Latest AI technologies)

 

 

Various applications of AI in utilities and energy

 

The global energy industry is facing increasing problems due to increased demand, efficiency, shifting supply and demand patterns, and a lack of analytics required for optimum management. These difficulties are exacerbated in developing market countries.

 

The application of artificial intelligence (AI) in the power sector is increasingly reaching emerging economies, where it might have a significant influence since clean, inexpensive, and dependable electricity is crucial to growth. 

 

The difficulties can be solved over time by passing power industry knowledge to AI software businesses. 

 

When appropriately developed, AI systems can be especially effective in the automation of regular and organised operations, letting people deal with the power issues of tomography.

 

1. Fault prediction

 

Fault prediction, coupled with real-time maintenance and the development of optimal maintenance plans, has been one of the principal uses of artificial intelligence in the energy sector. 

 

In an industry where equipment failure is widespread and can have serious implications, AI paired with proper sensors can be effective in monitoring equipment and detecting faults before they occur, saving resources, money, time, and lives.

 

Preventive procedures, such as chemical agent sprays, are designed to avoid turbine shutdowns. IoT and AI are being used to improve (quantity, composition, and timing). 

 

Such developments are critical in a nation like Japan, which has the world's third biggest geothermal resources, especially in light of the lowering prices of competing renewable sources like solar electricity.

 

 

2. Management of renewable energy sources

 

When it comes to short-term energy forecasting and optimising equipment management, there are various AI projects and application cases. 

 

In addition, in typical wind turbine operations, and solar panel sensor data are evaluated using AI to determine the intensity of sunshine. This also aids energy units in calculating the lifetime value of a resource.

 

 

3. Maintenance facilitated by image processing

 

Drones are being used by the United Kingdom's National Grid to monitor cables and pylons that carry energy from power plants to homes and businesses. These drones, which are equipped with high-resolution still and infrared cameras, have proved very beneficial in fault detection due to their capacity to cover large geographical regions and tough terrain.

 

They have covered 7,200 kilometres of overhead wires. This material may be repurposed for noncommercial uses if the source is identified as IFC, a member of the World Bank Group. Then, AI is used to monitor the state of electricity assets and identify when they need to be replaced or repaired.

 

 

4. Energy efficiency decision making

 

Customers may connect with their thermostats and other control systems to monitor their energy use using smart devices such as Amazon Alexa, Google Home, and Google Nest.

 

Because of the digital revolution of household energy management and consumer products, autonomous metres will be able to employ AI to improve energy usage and storage. 

 

It can, for example, cause appliances to be shut off when power is expensive, or it can cause electricity to be stored via vehicle and other batteries when power is cheap or solar rooftop energy is plentiful.

 

AI can assist improve estimates of power consumption and generation, allowing for better production decisions. 

 

This is especially significant in the shift to renewables, which are sometimes inconsistent owing to their dependency on weather, wind, and water flows, as well as their need for fossil fuels for backup. 

 

Artificial intelligence-based projections paired with energy storage infrastructure can lessen the requirement for such backup systems.

 

(Similar reading: Uses of IoT In Energy Sector)

 

 

5. Disaster recovery

 

When Hurricane Irma hit South Florida in 2017, it took 10 days to restore electricity and light, compared to the 18 days it took to recover from Hurricane Wilma. 

 

This reduction in time was made possible by emerging technologies such as AI, which can forecast the availability of electricity and guarantee that it is provided where it is most required without negatively damaging the system.

 

Furthermore, AI systems can improve damage assessments and decision making through faster access to pictures and information (within the first 12 to 24 hours) after a crisis has passed.

 

(Similarly, Read about how AI tackles Climate Change)

 

 

6. Demand management for energy

 

AI-powered demand management system comprising a number of platforms primarily focused on managing the demand response of several devices operating in parallel. 

 

The effectiveness of AI systems improved user behaviour to optimise energy consumption by providing feedback on energy performance in buildings and solutions that measure and predict future consumption trends.

 

We may, for example, integrate energy flow management systems with huge industrial equipment like air conditioning units and furnaces to automatically shut them down when power is low.

 

Another example is Google and its AI-focused subsidiary DeepMind, which managed to lower data centre energy use by 15%.

 

 

7. Prevention of losses due to informal connection

 

Losses resulting from unauthorised connections provide yet another issue to the electricity sector. 

 

• AI might be used to uncover these informal relationships by detecting anomalies in consumption patterns, payment histories, and other customer data. 

• It can also enhance monitoring for them when used with automated metres.

• It can also aid in the optimization of costly and time-consuming physical inspections.

 

Brazil, for example, has profited from such solutions after experiencing a high incidence of nontechnical losses such as informal connections and billing problems. 

 

Furthermore, the University of Luxembourg has created an algorithm that analyses data from electricity metres in order to identify abomination.

 

 

8. Infrastructure management for energy and utilities

 

The innovation and disruption in AI and digital asset management technologies are now assisting us in employing machine learning algorithms to aggregate, compare, evaluate, and identify risks and opportunities across utility infrastructure to grow power production enterprises.

 

The development of artificial intelligence and machine learning will alter the way we assess and monitor our energy consumption requirements. 

 

The convergence of next-generation pilot projects and application cases is establishing new methods for restoring nonrenewable resources in the future.

 

And, with technology servicing the demands of future consumption, energy and utility infrastructure management has the potential to store a limitless number of chances for humanity.

 

 

Conclusion

 

The utility and energy industries are getting increasingly complicated, so much so that participants can no longer manage all of the information they require using traditional technologies. 

 

They should now employ AI in its entirety, including natural language processing, deep learning, and reinforcement learning, to mitigate the consequences of the new reality.

 

As we enter the new era, we've witnessed how AI may alleviate pain points and aid with vital transitions. However, although AI has many benefits, it also introduces new 'digital challenges' that utilities have never encountered before.