Recent collaboration between Microsoft Corp. and United Nations Framework Convention on Climate Change (UNFCCC) is a testament to the power of Artificial Intelligence (AI) and advanced data technology in the global green movement. By creating a new AI-powered platform and global climate data hub, the partnership allows for a more efficient and accurate measurement of emissions. Naturally, this eases the process in providing tools for carbon reduction strategies, and better assist countries in meeting their climate goals.

As the world grapples with the escalating impacts of climate change and endeavours to meet ambitious targets, the need for substantial technological advancements in the energy transition becomes increasingly apparent. AI, with its unparalleled capabilities in data analysis and machine learning, is already making a significant impact across industries. Relative to climate change, the integration of AI can potentially contribute largely as a transformative force in energy production, distribution and consumption, unlocking innovative solutions to age-old environmental challenges.

Revolutionising Renewables and Precision Forecasting

Renewable and clean energy sources, such as solar and wind, offer the promise of reducing global emissions. However, these sources pose a unique challenge due to their inherent intermittency. The amount of energy generated from solar panels or wind turbines depend on the weather, which can be unpredictable, creating a significant hurdle for ensuring energy security. Integrating these clean energy sources into our existing energy system then becomes a complex task, as the fluctuating nature of renewables requires careful balancing to meet the constant demand for electricity.

This is where weather prediction plays a crucial role. By accurately forecasting weather patterns and predicting when renewable sources will generate the most energy, utilities can better plan and optimise the flow of energy through our grids. Predicting periods of high wind or intense sunlight allows for strategic adjustments to ensure a consistent power supply, reducing the challenges associated with intermittent energy sources.

To bring weather forecasting accuracy up to the standards required to safely integrate renewables into the grid, more computing power is needed, and AI capabilities can be a revolutionary solution.  AI algorithms can process vast amounts of data more efficiently and excel at recognising complex patterns and relationships within data that may not be apparent with conventional methods. This results in a more precise weather forecasting, with almost real-time updates as weather conditions change. The longer these AI algorithms are in action, the more they learn and improve, bringing our dreams of precision forecasting into reality.

Besides revolutionising weather prediction, AI is also being harnessed to make informed decisions across the entire life cycle of renewable energy projects. By analysing extensive datasets on weather patterns, geographical features and environmental factors, AI algorithms can identify the best sites for building renewable energy plants by pinpointing locations that will provide maximum energy yields.

Even the design of renewable energy products such as solar panels and wind turbines can be enhanced using AI. In the UK, National Grid Electricity System Operator is working on creating an AI-powered tracking system that matches cloud movements with the exact locations of solar panels. This will allow the company to better predict how clouds will affect the generation of solar energy, eventually enhancing and optimising solar energy generation.

Machine learning models are also being used to ensure that renewable energy systems are more efficient but also tailored to specific environmental conditions, enhancing the overall reliability and performance of renewable energy systems. NextEra Energy, one of the largest renewable energy companies in the world, has tapped on AI algorithms to analyse wind data and turbine performance in real-time. Equipment is continuously monitored by AI-driven tools to ensure smooth operations and prevent unexpected disruptions.

AI is also enabling predictive maintenance; anticipating equipment failures and outages by analysing data from sensors and historical performance, then dispatching resources to address potential issues. This predictive approach minimises system downtime, reduces maintenance costs in the long run and optimises the overall lifetime maintenance of renewable energy systems.

For Enel, a multinational energy company, incorporating AI has resulted in reduced system downtime by up to 30% and maintenance costs by 20%. By analysing sensor data and equipment performance, the company is able to anticipate and prevent potential failures in their power plants, leading to improved operational efficiency, reduced operational risks and extended asset lifespan.

Malaysia’s national utility, Tenaga Nasional Berhad (TNB), has similarly begun applying . By identifying and prioritising cable maintenance based on real-time analysis of the grid’s performance, the company is able to reduce the occurrences of cable breakdowns and subsequent power cuts. Over 5 years, this model is expected to result in savings of around RM30 million over five years, as well as a significant reduction in the System Average Interruption Duration Index (SAIDI).

SAIDI is a recognised global measure of system reliability, providing the average duration of electricity interruption experienced by consumers in a year. The goal is to keep this figure as low as possible. In 2022, Malaysia’s SAIDI was just 45.06 minutes per customer – among the best in the region and outperforming markets around the world.

As AI’s advanced algorithms provide increasingly accurate predictions for power demand and production, grid operations are expected to be further optimised to ensure that energy supply aligns seamlessly with consumer demand. At the same time, it will pave the way for more accurate predictions in power demand and production, allowing a more seamless alignment between energy supply and demand, and better integration of renewable power into the grid.

AI’s Versatility in Energising the Industry

Another pivotal area where AI is expected to play a ground-breaking role is in innovating energy storage solutions. As more intermittent sources are integrated into the grid, more storage solutions will be needed to address the issue of variable energy outputs. By storing excess energy during peak generation times and releasing them into the grid when needed, energy storage makes it possible for variable renewable energy to be integrated into the system without negatively impacting the security of supply.

Developing the necessary storage solutions for increased renewable energy integration is, by itself, one of the biggest challenges in the energy transition and with AI in the picture, storage solutions can be developed and managed with maximum efficiency.

Developing the necessary storage solutions for increased renewable energy integration is, by itself, one of the biggest challenges in the energy transition and with AI in the picture, storage solutions can be developed and managed with maximum efficiency. For instance, AI can be used to manage battery charge and discharge cycles based on real-time demand of energy. It can also be used to monitor battery health, or even accelerate the development of new energy storage materials and solutions.

AI is already helping to develop systems that capture emissions and curtail the environmental footprint associated with energy production and consumption. These AI-based monitors can not only pinpoint emission sources but also empower stakeholders to implement targeted strategies for more sustainable and eco-friendly energy infrastructure.

Building an AI-Friendly Ecosystem

To ensure the seamless integration and optimal utilisation of AI systems, creating frameworks and standards for the use of AI in the energy sector is crucial. Implementing standards will foster interoperability that warrants secure systems from different providers can work together efficiently. They also play a crucial role in guiding the ethical and responsible use of technology.

The World Economic Forum (WEF)’s ‘AI for the energy transition’ principles provide an excellent foundation for this, outlining essential steps to leverage AI in unlocking new efficiencies and opportunities towards the green movement. The nine principles, for designing, enabling and governing the use of AI in the sector, serve as a blueprint for safely and responsibly adopting AI, accelerating the energy transition, and lay the groundwork for a sustainable and green future.

Standardisations aside, the successful integration of AI into the energy sector will hinge, most importantly, on the collaboration between AI developers, energy companies, policymakers and communities, to create an ecosystem where knowledge exchange thrives.

Taking a collaborative approach will help align technological advancements with regulatory frameworks, ensuring compliance with legal requirements and ethical standards as AI technology matures and expands in reach.

As this is happening, it is important to engage local communities in the AI dialogue. As a revolutionary technology with potentially unlimited abilities, AI will, understandably, be met with apprehension by some sectors of the public. An open, community-centric approach to developing AI tools will go a long way in promoting transparency and building trust with end consumers. A deeper understanding of how AI technology can address the specific needs of the community will, in the end, will contribute to the overall acceptance of AI applications.

A Transformative Technology – If We Do It Right

Today, continued research and development is still needed to refine AI applications and make them more adaptable, efficient, and aligned with the evolving needs of the energy sector. By creating a supportive regulatory environment and fostering collaboration, we have the chance to propel AI technologies forward to lay the path for our sustainable future and mitigate the impacts of climate change’s worst-case scenario.

While the path to a sustainable future remains complex, the emergence of AI offers a beacon of hope. From grid management to predictive maintenance, AI-driven innovations are paving the way for a more resilient energy ecosystem. Realising its full capabilities is contingent upon the cooperation of all parties. The collective effort between governments and industry stakeholders can harness the power of AI to build a future powered by clean energy and environmental responsibility.

Quoting UN Secretary-General António Guterres, he stresses the importance of collaboration in the upcoming years, “We must all work together to develop frameworks and systems that enable responsible innovation. We must seize the moment, in partnership, to deliver on the promise of technological advances and harness them for the common good.”