AI: the secret to unlocking the potential of renewable energy?

Artificial Intelligence (AI) and machine learning are current buzz words being applied to many different aspects of society and it is safe to say that these terms generate a significant degree of hype. However, recent research and practice does suggest that AI is likely to play an important role in the future of the renewable energy sector. Not only could it help to cut costs and maximise efficiency, but also accelerate the transition away from fossil fuels. In the current climate of anxiety over not achieving climate targets, AI may be part of the answer. In this article, the Institute explores the role AI currently plays in our use of renewable energy, as well as its potential going forward.

How can AI aid the global energy transition?

Design and Manufacturing

AI can play an integral role in creating renewable energy from the outset of the process, i.e. the design and manufacturing stages of various renewable energy technologies.

AI is currently being used to run trials on new materials for solar panels which may mean that in the future, they do not require as many rare earth metals to produce.[1] A process that would usually require thousands of trials, AI will help automate and speed up these tests, as well as the analysis of the results. It is also hoped that this process can reduce the temperatures needed to create solar panels, making the overall process easier, cheaper and more sustainable.

Furthermore, not only will this help the design and production phase of solar energy, it will also have a positive impact on the end of life process for solar panels. With fewer components made up of rare metals, panels should become more recyclable than they are currently.

The first example of this type of AI being used was the Autonomous Discovery Accelerator (ADA), launched in Canada and cited as the world’s first self-driving laboratory.[2] While still relatively early days for this technology, a collaboration of 23 countries means that this machine learning should soon be more widespread across the global renewable energy industry.

This research will not apply only to solar panels, but other forms of green technology, such as wind turbines which generally require rare metals for their generators and motors.[3]

Cost and Waste Reduction

We have seen above how AI can help save costs in terms of the materials used and overall time efficiency of processes within the renewable energy sector. We will also see later in the article how AI can improve efficiency when generating renewable energy which in turn helps to cut costs.

The ability of AI to help cut costs and waste in the sector is undeniable. A 2022 U+ report estimated that using AI in conjunction with wind farms could save $1.3 trillion by 2030.[4]

AI technology is already being used in smart home heating, air conditioning systems and lighting, to help consumers use their systems in the most efficient way for their building. In reducing the amount of energy that is wasted, this in turn helps to reduce money spent on utilities. As AI progresses and helps to reduce costs further for providers, we should then see these savings being passed to consumers, making renewable energy a more economical option for all. At a time in which energy costs have never been higher, it is easy to see the appeal of using AI in the renewable energy industry.

Increasing Efficiency

AI and machine learning can help to improve the design and manufacturing processes in the renewable energy sector, but perhaps most crucially, it can also vastly improve efficiency while energy is being generated.

This can be seen with both wind and solar power, two forms of clean energy that are not always stable, and often affected negatively by variations in weather conditions. Forecasting programmes, which use constant data from turbine and solar panels, can continuously train algorithms to predict weather more accurately.

One such example of this is IBM’s Sunshot Initiative which combines a range of data, both current and historical, to improve solar forecasting. Reports suggest that there has been as much as a 30% improvement in the accuracy of the forecasting, resulting in more energy being produced.[5]

“We found that improved solar forecasts decreased operational electricity generation costs, decreased start and shutdown costs of conventional generators, and reduced solar power curtailment,” – Hendrik Hamann, Chief Scientist for Geoinformatics at IBM[6]

In India, a company called ThingsCloud have used similar technology, coupled with solar, to achieve 20-30% greater energy efficiency and reduce operation costs by 50%.[7]

In regards to the energy grid more widely, the same technology can help to predict surges and falls in demand in order to ensure stability. These processes are already relatively widespread, for example, AI can analyse the data from smart meters more quickly and efficiently than humans, to predict where and when energy is needed, as well as how best energy can be stored.

The world now has more data available than could ever be downloaded or analysed by humans; 175 zettabytes globally according to the International Data Corporation (IDC).[8] Therefore it is clear that we will have to embrace machine learning in some capacity in order to make use of some of this data to combat climate change.

Accelerating the Transition

One of the most important ways in which AI can help is to speed up the transition from fossil fuels to renewable forms of energy; amidst an atmosphere of concern over not reaching key targets, plus the enormity of shifting society away from coal, oil and gas, AI could help with many of the problems encountered.

In 2022, emissions from fossil fuel generated energy hit an all-time high, rising by 1.3%[9]; obviously the transition needs to be sped up significantly. We cannot by any means rely on AI fully to achieve the switch for us, but through many of the processes discussed above, AI can help to make renewable energy more widespread, more reliable and cheaper, therefore alleviating concerns surrounding cost and supply.

Criticisms of AI

We have read how AI can achieve a number of positive effects in the renewable energy sector and the overall concept is currently very popular. However, AI and machine learning also have many critics, and are technologies that can foster a general anxiety over passing human roles over to machines.

Worries over a reduction in available jobs are at the forefront of criticism. The World Economic Forum estimates that the increasing use of AI could create 69 million new jobs by 2027, but also make 83 million positions redundant.[10] However, despite the rise in AI, employment in the renewable energy sector is still booming; IRENA reports that the industry could see 38 million jobs available by 2030.[11] Combined with the fact that humans simply cannot process data at the same speed as machines is a clear argument to shift some industry roles over to AI.

Other critics argue that the cost to benefit ratio of using AI does not work in our favour.
The cost of training AI to be able to work to the capacity required is generally very high and may even go some way to negating the benefits. Costing will change drastically depending on the project, and more data is needed to analyse the effect on the renewable energy sector. However, custom AI solutions can cost anywhere up to $300,000, before any money is spent on further training and operations. [12] Therefore, significant returns are required in order to make this process worthwhile.

Case Studies: AI in Practice

Both of the case studies below are still in the very early stages of their respective projects and so they can tell us little about the effectiveness of AI in renewable energy. However, they certainly are ground breaking and worth keeping an eye on over the next few years as a marker of success in the industry.

South Korea

As of April 2023, the Wando-Guemil offshore windfarm, located in South Korea will be introducing an artificial intelligence-based cable-layout optimisation technology called KLOC.[13] The technology is provided by Kinewell Energy and allows for economically optimised inter-array cable layout, which prioritises the optimisation of capital costs against operational costs such as electrical distribution losses. It is expected to help maximise the value of this specific wind project.


In 2023,DEME and Jan De Nul will be constructing an ‘artificial energy island’ in the North Sea, off the coast of Belgium.[14] This island will be the first of its kind globally. It will integrate direct current (HVDC) with alternating current (HVAC) and work to connect Belgium with the UK and Denmark. In the long term the project will connect all offshore windfarms to mainland Belgium; this is expected to take place by 2030.


The role of AI in increasing efficiency, reducing costs and accelerating the transition is all very closely intertwined. Much of it can be boiled down to the fact that humans simply don’t have the capacity to analyse all of the data present that can help us to achieve net zero.

While AI will always have its critics, and does have tangible down sides, it is clear that we will need to embrace it in at least some capacity in order to help the monumental task of moving away from fossil fuels. As we look past 2023, we can surely expect to see more and more renewable energy projects employing AI and machine learning.

Are you interested in learning more about AI in the renewable energy sector? Register your interest below so that the Institute can keep you up to date with future courses and seminars.



    [3] As above



    [6] As Above