Digitalization in energy technology: Modern AI methods for data acquisition; [Digitalisierung in der Energietechnik: Moderne KI-Methoden zur Datenbeschaffung]

Abstract

Digitalization is changing energy technology by increasing the amount of data available from technical systems, networks, sensors, and operational processes. Modern artificial intelligence methods can help transform this growing volume of information into useful knowledge for analysis and decision-making. The article discusses the role of AI-based approaches in data acquisition within energy-related applications, where reliable and timely information is important for understanding system conditions and improving operations. Automated methods can support the collection, classification, processing, and interpretation of data from different sources. When combined with appropriate geospatial and technical information, these methods can help identify patterns, detect irregularities, and improve the management of energy infrastructure. The use of AI also creates opportunities to reduce manual processing and make complex datasets easier to handle. At the same time, successful digitalization depends on data quality, suitable system integration, transparent processes, and clearly defined application goals. The article therefore places modern AI methods within the broader development of digital energy technology rather than treating artificial intelligence as an isolated solution. The topic illustrates how data-driven methods can support more efficient technical workflows and contribute to the continued modernization of energy systems. Data governance and interoperability remain important alongside technical capability. Energy organizations need methods that allow information from different devices and systems to be combined without creating isolated data environments. This makes standards, reliable data structures, and appropriate validation procedures important parts of practical AI-supported digitalization.