MMK in Heilbronn - 24. Nov. 2026
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The challenges in the energy sector

The demand for electricity - and in particular the demand for electricity from renewable sources - is increasing enormously. The main reasons for this are the stricter climate targets and the increasing number of electric cars. Converting the energy system to sustainable energy sources and a networked energy supply will be the key challenge for the industry in the coming years. This will be accompanied by a strong decentralization and individualization of generation and consumption. The traditional value chain, which was characterized by a small number of central power plant operators, is being replaced by the decentralized energy industry. The resulting increase in complexity opens up a variety of opportunities and potential for the development of new, innovative business models, particularly through the use of data-driven methods to manage complexity.

Data science methods are increasingly being used in various areas of the energy sector, as the following examples show:

Thanks to learning algorithms and AI-supported analyses of weather, sensor and consumption data, more accurate forecasts of electricity generation and consumption can be made, thereby increasing grid stability and security of supply. For example, AI can be used to calculate and predict more accurately how much wind will blow the next day or how strong the sun will shine and thus react to an expected shortage or surplus of wind or solar energy. Thanks to learning algorithms, the systems can adapt flexibly to the weather and consumption situation.

Furthermore, data methods are used to increase performance and thus reduce costs in the area of electricity generation. In intelligent wind farms, wind turbines communicate with each other and take into account the effects of the resulting air turbulence on the performance of downstream wind turbines when optimizing the alignment of their own rotors in the wind. Predictive maintenance methods can be used to reduce maintenance costs and increase the service life of the turbines.

Innovations through data science and AI are conceivable in various areas of the energy sector. As an application example for bird and species protection and thus for increasing the acceptance of wind turbines shows. Intelligent camera systems use neural deep learning networks to detect bird species that are sensitive to wind power and briefly switch off the wheel if a bird gets too close to the turbine.

With a strong and robust portfolio, almost along the entire energy value chain, the company is Siemens Energy Stakeholder and shaper of the energy transition worldwide. By driving forward the corporate goal "We energize society", Siemens contributes directly or indirectly to most of the United Nations Sustainable Development Goals (SDGs). AI and image recognition are also used at Siemens Energy. One example is the intelligent inspection of overhead lines using drones (SIEAERO). Using optical and 3D laser sensors, up to 12,000 images and a digital twin of the overhead line are created, which are automatically searched for damage using SIEAERO's software.

The fourth industrial revolution holds great potential for increasing resource efficiency in production in the energy industry. For example, the wind turbine manufacturer ENERCON Ltd. is driving forward the automated and modularized production of large wind turbine components. The new EP5 turbine range, with a blade diameter of up to 175 meters and a nominal output of 6 MW, is an important cornerstone of the new product and market strategy. ENERCON is thus developing efficient turbine types that meet the requirements of a renewable energy system and highly competitive markets.

Medium-voltage switchgear is a critical component of electrical energy distribution. Urbanization and economic growth continue to drive the expansion of infrastructure. A large proportion of the MV systems used in the electrical grid contain SF₆ gas, which is supplied via a High global warming potential (GWP) available. The environmentally friendly systems from Schneider Electricoffers a way to reconcile the consequences of growth with a sustainable approach to reducing your greenhouse gas footprint. In addition, Schneider Electric is electrifying and digitizing fossil consumers and connecting them in an IoT. This form of Electricity 4.0 enables the distribution of electricity according to generation and demand.

ABB exceeds the majority of its sustainability targets for 2022 and has reduced greenhouse gas emissions by 58% since 2013. The company has set itself clear and ambitious targets with its new 2030 sustainability strategy. At ABB Smart Power's production facility in Frosinone, Italy, for example, the company has succeeded in eliminating the disposal of production waste in landfills - 14 years ahead of the European Union's circular economy package, which aims to reduce the landfill rate for waste to a maximum of 10 percent by 2035.

In March 2023, Munich Management offers the opportunity to learn solutions from the leading figures in the industry and to make a decisive contribution to Germany as a business location through the change processes presented and the associated innovations.

Over 80 renowned managers and executives from various industries will speak on this topic and present their solution concepts. Get to know best practices and the success patterns of successful companies at the 30th Munich Management Colloquium!

Among others, the following speakers from the energy sector will give talks on the topic of "Innovation - Sustainability - Resilience":