From improved weather predictions to smart power grid management, AI offers relevant solutions to some of the challenges of the energy transition, particularly in developing countries.
In a world where energy demand continues to grow – global electricity consumption rose by 2.5% per year between 2010 and 2019 – artificial intelligence (AI) is emerging as a key tool to address this increase and optimize the exploitation of renewable sources. These technologies not only overcome the obstacles of variability in renewable energies but also make access to energy more reliable and affordable in rural areas.
Developing countries face a dual challenge in their energy transition: to meet growing demand while limiting their dependence on fossil fuels, which are often costly and polluting. For example, according to the International Energy Agency (IEA), energy demand in Africa is set to increase by 60% by 2040, while the average cost of oil imported into these countries was $80 per barrel in 2022. In this context, renewable energies, particularly solar and wind power, appear to be a key solution, but their implementation remains complex due to their intermittent nature and the fragility of the infrastructure.
AI can play a key role in improving the management of renewable energies and optimizing their production, storage, and distribution. According to the IEA, its use could increase the power grid efficiency by 20% through minimizing losses and perfecting the distribution of electricity, thus promoting better integration of solar and wind power into national infrastructures. With its optimization capabilities, AI is a strategic tool for combining energy expansion and sustainable transition.
Forecasting renewable energy variability
One of the main challenges facing renewable energy is its dependence on natural conditions, such as sunshine or wind strength. For example, a cloudy day can significantly reduce solar energy production, while an absence of wind renders wind turbines inefficient. To overcome this variability, AI offers predictive models capable of anticipating these fluctuations with great accuracy.
Recent studies show that machine learning algorithms can simultaneously analyze historical data and real-time weather information. For example, in sub-Saharan Africa, where solar energy is abundant but underexploited, pilot projects are using AI to predict periods of high sunshine and adjust production accordingly. A case in point is the Zagtouli pilot project in Burkina Faso, where a 33 MW solar power plant uses AI algorithms to optimize production based on solar forecasts. These predictions enable power grid managers to better plan energy distribution, reducing the risk of shortages or surpluses.
Smart grid management
In the rural areas of developing countries, electricity grids are often fragile and sparse. Integrating renewable energies into these grids poses technical challenges, especially due to their intermittent nature. This is where smart grids come in. These systems use AI to optimize energy distribution in real time, based on demand and available production.
In India, for example, smart grid projects combining solar power and battery storage have been deployed in remote villages. One noteworthy case is the solar microgrid project in the state of Uttar Pradesh, where solar-powered systems managed by AI have been installed in over 100 villages, providing reliable electricity to around 40,000 homes and reducing blackouts by 70%. These systems can balance the energy load, avoid blackouts, and guarantee a stable supply. In addition, AI enables local microgrids to be connected to renewable energy sources, offering a sustainable solution for electrifying rural areas without relying on expensive centralized infrastructure.
Predictive maintenance of energy infrastructure
Maintaining renewable energy installations, such as wind turbines or solar panels, is often costly and complex, especially in remote areas. AI offers an innovative solution: predictive maintenance. By analyzing data from sensors installed on equipment, AI algorithms can detect anomalies and predict failures before they occur.
For example, a study carried out in Morocco (Unimagec, 2023) showed that using AI to monitor wind turbines reduced maintenance costs by 20% and increased the lifespan of the installations. This approach is particularly suited to developing countries, where financial and technical resources are limited. By avoiding unforeseen failures, AI helps make renewable energies more reliable and economically viable.
Artificial intelligence is not just a technology of the future. It is an essential tool for tackling today’s energy transition challenges. By improving the prediction of energy production, optimizing the management of power grids and reducing maintenance costs, it offers concrete solutions to accelerate the adoption of renewable energy in developing countries. For policymakers, investing in these technologies represents an additional opportunity to foster sustainable development, improve access to energy and contribute to the fight against climate change.
However, this growth must not overshadow the energy and environmental footprint of AI deployment itself. Data centers, for instance, consume around 2% of the world’s electricity, according to the International Energy Agency, and require rare metals for equipment and increasing land use. A Boston Consulting Group study estimates that AI could reduce global greenhouse gas emissions by 5 to 10%– or 2.6 to 5.3 gigatons of CO2 equivalent– but this saving must be weighed against the 460 TWh consumed annually by data centres in 2022, a figure set to increase with the rise of AI.
By integrating AI into their energy strategies, governments can meet their populations’ immediate needs and lay the foundations for a more resilient and environmentally friendly future, provided they ensure a balance between the expected benefits and the associated environmental costs. The time has come to seize this opportunity and make AI a pillar of the global energy transition, while developing frugal and sustainable approaches to limit its ecological impact.
