Reference
AI-Driven Predictive Maintenance for Energy Storage
Our Work
Anyone who operates energy storage systems in the field faces a classic problem: Failures rarely give enough advance warning, and traditional maintenance intervals do not scale with a growing fleet of devices. Together with SENEC, we have developed a system that identifies patterns in operational data before they lead to failures—based on an artificial neural network trained on historical real-world data from over 75,000 storage units in the field.
Reliable detection through artificial intelligence
Our Client: Who Is SENEC?
Since 2009, the SENEC GmbH in Leipzig intelligent Energy Storage Systems and Storage-Based Energy Solutions. With more than 75,000 systems sold and a network of over 1,100 specialist partners, SENEC is one of Europe’s leading providers of innovative energy and storage solutions for single-family homes. Its product portfolio includes energy storage systems, solar modules, a virtual electricity account, and electric vehicle charging stations. Since 2018, SENEC has been a wholly owned subsidiary of EnBW Energie Baden-Württemberg AG and employs over 450 people at locations in Germany, Italy, and Australia.
AI-Based Maintenance System for Energy Storage Systems
Project Success Story
A neural network, trained on real-world data from more than 75,000 energy storage systems, identifies devices in the field that require maintenance before they fail. The project also included the handover of infrastructure to SENEC.
Key Areas of Collaboration:
Project Objectives
The goal was to detect energy storage systems in the field that require maintenance at an early stage—before a failure occurs. Reactive maintenance following a failure was to be replaced by data-driven, proactive diagnostics that scale reliably as the number of devices grows.
Key Factors
- Early detection of equipment requiring maintenance before it fails
- Training using historical real-world data from the entire storage portfolio
- Reliable Anomaly Detection in Live Operations
- Scalability as the number of devices grows
- Robust data pipeline for large volumes of data
- Full handover of infrastructure for independent operation at SENEC
Implementation
- Building structured time series from historical operational data as a basis for model training
- Development and Training of an Artificial Neural Network Using Python and TensorFlow Based on Real-World Data
- Implementing a High-Performance Data Pipeline with PostgreSQL for Importing Large Volumes of Data
- Validation, testing, and deployment of the model into live operation — including full handover of the infrastructure to SENEC
„IT Sonix is the perfect partner for us when it comes to implementing our projects. We value their high level of expertise, as well as their flexibility and ability to quickly and efficiently find and implement solutions for our complex requirements. With IT Sonix’s support, we were able to successfully complete key projects and achieve our goals for 2020/2021.“
Dr. Patrick Oesterling
Project Manager
Predictive Maintenance Features
Results of the Collaboration
The predictive maintenance system is now in live operation and reliably identifies storage units in need of maintenance—before a failure occurs. Service teams can plan interventions proactively rather than reacting after the fact, which directly translates into reduced costs and higher customer satisfaction. SENEC now has a complete infrastructure consisting of a data pipeline, a training environment, and a KNN model—which it can operate independently and scale to accommodate new generations of storage devices.
Additional Industry References
Learn more about our expertise and services in predictive maintenance.
Predictive Maintenance for Industrial Belts
Automated Forecasting for Assembly Lines Using Data Science …
AI-Driven Predictive Maintenance for Energy Storage Systems
AI-powered failure prediction for energy storage systems in the field — developed, trained, and deployed in live operations. …
Get Started on Your Project
Sebastian Wetzel
We understand that every project is unique. Our experts look forward to speaking with you to find customized solutions.
