AI can lower the cost of the clean energy transition in Africa

AI can lower the cost of the clean energy transition in Africa
AI can lower the cost of the clean energy transition in Africa

Oluleke O. Babayomi

Africa-Press – Eritrea. Artificial Intelligence has the potential to make the clean energy transition more affordable and efficient. Oluleke O. Babayomi explores how innovative financial mechanisms and enhanced lifecycle monitoring can make a difference in Africa.

An affordable clean energy transition is crucial for Africa’s sustainable development. Nonetheless, the high upfront costs associated with developing technologies like geothermal and hydropower pose a significant barrier to uptake. This could cause a carbon lock-in effect because African actors cannot afford to replace their carbon-burning infrastructure which commits them to carbon emissions for many years to come.

Artificial Intelligence (AI) could be a powerful tool to mitigate these costs and offer innovative solutions for financing upfront costs and managing the new technology across its lifecycle. By leveraging AI, Africa can accelerate its transition to clean energy and make it more affordable, thus promoting economic growth and energy access across the continent.

By using the latest data and AI tools at every stage the cost of energy infrastructure projects can be reduced and the transition to clean power made more affordable across Africa.


The high financing costs of infrastructure projects in Africa do not reflect the region’s low loan default rate. According to Moody’s Analytics, Africa’s ten-year default rate of 1.9 per cent is significantly lower than those of Europe, Asia and the Americas.

By leveraging AI algorithms, risk can be more accurately modelled on a country-by-country basis, financing costs can be better matched to specific debtors, and costs lowered. The potential reduced cost of financing can attract more funding to African countries for energy transition infrastructure. Incentives such as tax breaks and subsidies targeted at AI-enhanced clean energy projects can further stimulate investment and adoption making clean energy cheaper at all points of the project.

Resource exploration

Resource exploration and assessment are critical and costly early steps in the development of clean energy projects. The exploration of geothermal energy is often hindered by the high cost of initial geological surveys, the drilling of deep wells, and the expensive technology and equipment required.

There is a high financial risk associated with uncertainty in finding productive geothermal resources; conventional methods can only ascertain viable wells after costly drilling exercises. AI can significantly reduce the cost and time associated with these stages. AI can analyse geological data more efficiently than traditional methods. Machine learning algorithms can process vast amounts of seismic, magnetic, and gravity data to identify promising geothermal sites, thereby reducing the need for extensive and expensive exploratory drilling.

Optimising construction

The construction phase of clean energy projects is often resource-intensive and costly. AI can streamline this process by optimising construction schedules, resource allocation, and logistics. For geothermal plants, AI-driven 3D modelling and simulation tools can predict potential construction issues and optimise the planning and execution of projects, reducing delays and cost overruns. Adopting AI applications in construction projects has already led to significant cost savings.

Operation efficiency

Once clean energy plants are operational, AI can continue to play a vital role in reducing costs by optimising performance and maintenance. AI systems provide real-time monitoring and predictive maintenance for both geothermal and hydropower plants. In geothermal plants, AI can optimise operational parameters such as flow rates and pressures to maximise efficiency and minimise costs. Predictive maintenance powered by AI can foresee equipment failures before they occur, reducing maintenance costs and preventing unplanned downtime. AI-driven predictive maintenance can result in substantial cost savings in operational expenditures for geothermal plants.

AI-driven predictive models can also optimise energy generation, storage, and distribution, improving efficiency and lowering the cost of generation and storage technologies. Using AI to improve demand forecasting can reduce the costs caused by the need for backup power generation plants for peak-demand periods. This also maximises the utilisation of installed energy resources by reducing downtimes for repairs. These can be integrated into new projects from the ground up, rather than being retrofitted onto old ones. This reduces the cost of energy production making it cheaper for consumers.

Decommissioning and environmental restoration

At the end of a power plant’s operational life, it needs to be properly decommissioned. Think of it like retiring an old car; you don’t just stop driving it and leave it parked outside your house, you must dispose of it properly to avoid environmental damage and ensure safety. For renewable energy plants, such as geothermal power plants, decommissioning involves removing all the equipment, and restoring the land to its original state or repurposing it for other uses.

AI aids in the cost-effective decommissioning of clean energy plants and environmental restoration, ensuring that these processes are as efficient and economical as possible. For geothermal plants, AI can develop accurate cost estimation models for decommissioning, helping operators plan financial resources and resource allocation optimally. AI can also assist in planning environmental restoration, predicting the most effective methods for returning the land to its natural state, thus minimising environmental impact and associated costs.

AI holds transformative potential for making the clean energy transition more affordable and efficient in Africa. By introducing innovative financial mechanisms and reducing costs in resource exploration, assessment, construction, operation, and decommissioning, AI can significantly lower the financial barriers to clean energy adoption. This not only accelerates the transition to cleaner energy sources but also promotes economic growth and energy access across the continent. As Africa continues to harness the power of AI, it can pave the way for a more sustainable and prosperous future, where clean energy is both accessible and affordable for all.


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