Sustainability through AI: increasing efficiency in the energy industry

It is our light, our washing machine and our local healthcare. Our need for a constant supply of energy has always increased, and for a long time a solid and reliable energy supply has been essential for survival. The need to reduce the share of fossil fuels in energy production brings with it a number of challenges. In particular, both grid utilisation and electricity prices will become more volatile. The existing infrastructure, from pipelines to power plants, will also need to be redesigned. Data and AI can play a crucial role in overcoming these challenges. How these technologies can be utilised is the subject of this blog post.

Climate change and the challenge of the energy industry

Climate change is one of the biggest challenges of the 21st century. Rising global temperatures, more frequent extreme weather events and changes to ecosystems are just some of the serious consequences that humanity is already feeling today. A key driver of climate change is the emission of greenhouse gases, in particular carbon dioxide (CO₂), which is largely released by the combustion of fossil fuels in the energy industry.

In order to combat climate change and limit global warming to a tolerable level, emissions must be drastically reduced. The energy industry plays a key role in this, as it is responsible for a significant proportion of global carbon emissions. Two basic strategies are available to reduce emissions in this sector: more efficient use of energy and a general reduction in energy consumption. The use of artificial intelligence (AI) can achieve significant increases in efficiency here and thus significantly improve the sustainability of the energy supply.

Precise AI-based predictions for energy generation and consumption

One of the main advantages of using AI in the energy industry is the ability to make precise predictions about electricity generation from renewable energies and the energy consumption of industry and households. Conventional forecasts are based on historical data and simple statistical methods, which are often unable to fully capture the complexity and dynamics of modern energy systems. In contrast, AI models use advanced machine learning algorithms to recognise patterns and trends in large amounts of data to make more accurate predictions.

By integrating various data sources such as weather forecasts, historical consumption and generation data, socio-economic factors and real-time information, AI-powered systems can predict energy demand on an hourly, daily or even weekly basis with high accuracy. These precise forecasts enable energy suppliers to plan their generation resources efficiently and thus avoid overcapacity or bottlenecks. This leads to better utilisation of generation plants and reduces the need for expensive reserve capacity.

Preventive and value-orientated maintenance using AI algorithms

Another area in which AI enables significant increases in efficiency is the maintenance and servicing of operating equipment. By aggregating all available data on a standardised data platform, AI algorithms can be used to carry out continuous analyses and identify potential faults in systems such as transformers at an early stage. This predictive maintenance prevents unplanned outages and extends the service life of the systems, resulting in a more sustainable use of resources.

AI-supported maintenance systems can also reduce the costs of repair and maintenance work and increase plant availability by combining information about the health of production plants and their economic yield. This enables targeted planning and deployment of maintenance personnel and resources, which further increases efficiency and at the same time improves operational reliability. RWE Generation has implemented this "value-based maintenance" approach together with adesso in a multi-year project at several power plants in Europe.

Dynamic pricing and new marketing strategies

From the beginning of 2025, all electricity suppliers must be able to offer their customers a dynamic electricity tariff. Traditionally, customers receive a time-variable tariff that provides an individual labour price for each hour - depending on the exchange price. The prerequisite for such dynamic tariffs is an intelligent metering system (smart meter) that is capable of recording the necessary data and exchanging it with the market participants via a gateway. The electricity supplier is faced with the challenge of developing an attractive dynamic tariff and not just reflecting the "exchange price" and passing it on to the customer. AI systems can develop flexible price models by analysing large volumes of data and taking into account numerous factors such as consumption behaviour, exchange price, weather conditions and grid utilisation. These dynamic price models enable energy suppliers to adjust their prices almost in real time and thus optimise both generation and consumption. This not only leads to more efficient energy utilisation, but also opens up new marketing strategies. Energy suppliers can provide their customers with personalised tariff offers based on their individual consumption profiles. This increases customer satisfaction and at the same time promotes conscious consumption behaviour, which helps to reduce energy consumption and smooth out peak loads.

Personalised recommendations for saving energy

German households are responsible for around 25 per cent of total electricity consumption in Germany, which means that savings can have a significant impact on the overall balance. By analysing the consumption behaviour of household customers, AI systems can also provide personalised recommendations for saving energy. In particular, there is an increasing focus on optimising self-consumption in households that have a PV system in combination with a home battery storage system and/or an electric car. Typically, the midday hours are the hours with the highest solar feed-in, but often the residents are not at home. The electricity can then only be partially utilised, so it makes sense to temporarily store the electricity in a battery. The household can draw electricity from the battery in the morning and evening. The charging processes of the electric car in particular can help consumers to optimise their energy consumption or shift it to more favourable times by providing tailored information, thereby saving costs and reducing their ecological footprint. AI algorithms that have been trained on a broad database with lots of customer data are particularly suitable for this.

Networking and controlling decentralised energy producers & consumers

By using AI-supported control systems, these decentralised generators can act as a virtual power plant that is able to generate and supply energy efficiently. A central control centre manages communication with the decentralised systems and coordinates joint marketing. This enables better integration of renewable energies into the electricity grid and contributes to the stability and sustainability of the energy supply. In the VideKIS research project, adesso was able to show that AI-based systems can network thousands of systems and market them on the energy and balancing power markets. Such approaches not only promote the use of renewable energies, but also increase the flexibility and resilience of the electricity grid. Virtual power plants are also able to include consumers in the system pool so that the grid can be relieved by a short-term increase in consumption in the event of an electricity surplus.

Conclusion

The use of artificial intelligence in the energy industry offers a wide range of opportunities to increase efficiency and promote sustainability at the same time. Through precise predictions of energy demand, early detection of disruptions, dynamic pricing, personalised recommendations for saving energy and the networking of decentralised generators, the energy industry can significantly reduce its emissions and make an important contribution to climate protection.

Despite the numerous advantages, there are also limits to the use of AI in the energy industry. Forecasting accuracy decreases, particularly for longer-term forecasts, as unforeseeable events and the effects of new regulations in the energy system are difficult to model - especially when forecasts are based on historical data. In addition, the introduction of AI modelling requires significant investment in infrastructure and data management. Without solid data quality and availability, the potential of AI applications cannot be fully utilised.

You can find more exciting topics from the world of adesso in our previous blog posts.