The energy sector is caught in a whirlwind of changes and challenges. The increasing pressure to shift to renewable energy sources is jeopardised by rising geopolitical unrest, fluctuating energy prices, and unpredictable weather. However, technology can play a role in accelerating this energy transition by improving transportation, storage, efficiency, and more.
But, as we make the energy transition, questions around resilience, scale, and return on investments need to be answered.
Energy transition involves addressing several issues, from managing the legacy grids to optimising the energy networks. New energy options like hydrogen, geothermal energy in addition to renewables are driving the transition agenda for multiple energy-dependent industries.
This means taking a different approach to manage the lifecycle of geographically distributed and remote assets and thinking innovatively about addressing the new risks arising from climate change.
With the convergence of technologies that make artificial intelligence (AI) a powerful tool, energy companies are using it to reshape how we produce, distribute, and consume energy.
Today, AI is at the forefront of transformation as it enables power plants to operate at peak efficiency, anticipate equipment failures before they occur, and streamline the integration of renewable sources as an alternative energy option. AI will thus play a critical role in determining the right source of energy based on the availability, emission exposure and net zero targets with commercial considerations.
Scripting a sustainable energy future
AI is not only optimising the energy sector but also forging a green path for sustainability. With its ability to predict energy demand patterns, AI can enhance grid efficiency and mitigate carbon emissions which form the cornerstone for a cleaner, more environmentally responsible energy landscape.
For example, AI can predict energy demand patterns more accurately by analysing historical data, climate changes, and other relevant factors. This helps utilities plan for and allocate resources effectively, reducing energy wastage.
Empowers renewable energy systems for efficiency, reliability, and affordability
AI algorithms forecast renewable energy production with unprecedented accuracy, allowing grid operators to integrate solar and wind power seamlessly. Furthermore, AI-driven smart grids can swiftly adapt to fluctuations in renewable energy output, ensuring a stable and reliable energy supply.
For instance, during periods of abundant sunlight and low energy demand, AI can forecast surplus energy production from solar panels and direct it to charge batteries. Later, during peak demand hours or when renewable generation is low, AI can optimise the distribution of stored energy to meet the city's needs efficiently. AI is also capable of integrating renewable sources and storage technologies.
Data-driven AI can optimise energy management
AI's ability to analyse massive datasets in real time is instrumental in improving energy management. AI-powered predictive maintenance systems use data from sensors and equipment in power plants or oil rigs to forecast maintenance needs.
By analysing historical data and real-time sensor readings, AI can detect anomalies, identify potential equipment failures, and schedule maintenance proactively. For instance, a wind farm can use AI to predict when a turbine blade needs maintenance, ensuring optimal performance and reducing costly unscheduled repairs.
Enhance energy conservation practices across sectors
AI can analyse data from buildings, industries, and transportation and optimise energy consumption through intelligent control systems, promoting efficiency and sustainability.
These cutting-edge technologies help enterprises benefit from optimisation of energy utilisation, reduce expenses, and actively participate in a greener tomorrow. One illustrative case is the use of AI-driven systems that have the capacity to maximise energy usage by allocation of resources based on factors such as weather conditions or energy prices.
Predicting emissions and help with mitigation plan
AI routines are deployed to monitor emissions though multiple sensors feeding to models which continuously measure emission from production operations, logistics and maintenance activities. These models predict future emissions including the reduction measures, change in raw material mix, energy source, and optimisation data. This helps industries plan achievable targets.
Challenges in AI adoption
Powering the energy transition with AI also presents several challenges. Firstly, there is the need for massive data sets to train AI algorithms effectively, which can be costly and require extensive infrastructure. Secondly, ensuring data privacy and cybersecurity in the energy sector is paramount, as AI relies heavily on data sharing and connectivity.
Additionally, there are regulatory and policy hurdles to navigate, as governments and organisations grapple with standardising AI practices in a complex and heavily regulated industry. Lastly, there is the issue of workforce readiness; preparing the energy sector's workforce to harness the full potential of AI technologies demands substantial investments in training and skill development.
The future outlook
The future outlook for the energy transition with AI is undeniably promising. It will enable even greater options of renewable energy sources into the energy grid. Advanced forecasting and real-time grid management will empower the use of these intermittent sources, reducing dependency on hydrocarbon fuels.
AI will play a crucial part in the electrification of transportation and heating systems, helping to minimise climate-altering emissions and promote energy efficiency. There will be a future where local communities can generate and manage their energy requirements.
In conclusion, the future of the energy transition with AI promises a greener, enduring, and resilient energy ecosystem. Unless we clean up our energy, it will be hard to achieve the goal of net-zero emissions by 2050 as part of the Paris agreement.
— The author, Balakrishna D.R. is Global Head of AI and Industry Verticals & Executive Vice President, Infosys. The views expressed are personal.
(Edited by : C H Unnikrishnan)
