AI in the energy industry

The energy transition, the increasing integration as well as deregulation of the market and the consumers' desire for self-determination pose enormous challenges for the energy industry.

Here you can find out how AI and data science can offer solutions and what added value they create in the various application areas!

Data & AI projects for the energy industry

We have used our experience from over 1,000 projects to develop a holistic system for data & AI projects, our [at] Data Journey:

Data Strategy: A consistent strategy forms the basis for the efficient use of data in your company. The goal is to develop a use case as quickly as possible.

Data LabHere, the use case idea becomes an analytical concept, which is tested in the next step. Then we develop your analytics or big data application in a test environment.

Data Factory: Use cases are industrialised into finished products. The absolute focus is on scaling and sustainable added value - which is why the focus here is also on the user.

DataOps: In this step, we operate and maintain your platforms and machine learning algorithms.

Projects of our customers


In recent years, as experts in the field of Data Science and AI, we have been able to different Implement projects in the field of energy management with our customers. Here you can find some of our references. If you have any questions of course with pleasure available. 


Anomaly in electricity consumption with Data Science
  • Introduced employees to data science and machine learning
  • Approaches and methods identified for detecting anomalies in electricity consumption
  • Necessary data science skills identified for further projects
Forecast of control energy AI
  • Identification of the sign of the balance with a quality of 83 % for positive balances
  • Significant improvement of the absolute deviation compared to simple updating
  • Walkforward model that is constantly improving with new data
Bunker modelling
  • Repository of technical, process and analytical approaches
  • Next steps to improve the quality forecast identifies
  • Interdisciplinary understanding of use case and Terminology
Credit rating service with data science
  • Modern, more fail-safe infrastructure
  • The internal analytics team is empowered to provide the credit rating service to develop independently
  • The developed Rest API can be used as a template for further use cases. be used
Predictive Plant Maintenance
  • More efficient maintenance shows savings potential of €5 million annual
  • The prototypical application creates transparency about historical Maintenance
  • Data strategy and platform architecture for AI
Load forecasting in the energy sector
  • Improvement of the forecast quality

  • More accurate prediction of individual distributors
  • Implementation of a transparent and high-performance forecasting system

Whitepaper - AI energy industry

- The white paper with best practices & concrete use cases -

Whitepaper - AI energy industry

Application areas of AI & Data Science in the energy industry

AI and data science have already made enormous progress in the energy industry and are being used successfully in the following application areas, for example:

Predictive Maintenance

Optimise plant maintenance and prevent premature wear through forecasting



Optimised deployment planning of equipment to realise efficiency gains 

Data Analytics

Data basis for improved planning of grid expansion and new plants



Ensuring the availability of fluctuating forms of energy and optimising marketing



Improving the customer experience through the use of modern communication tools such as virtual assistants and digital robots 

Churn Prediction

Identify customers at risk of churn at an early stage and retain them through targeted approaches and offers


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Facts about artificial intelligence in the energy industry

A study by the German Energy Agency (dena)  shows the importance of AI for the energy sector.


Three out of four respondents think AI will have a positive impact on the energy transition


Only 13 percent have already invested in AI or at least allocated a budget for it


Reason: only 17 percent are well or very well informed about AI

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Simon Decker

Simon Decker

Data & AI Projects

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