An effective MLOps workflow developed within the Datalab team
Waternet
Data & AI for the energy sector
A stable energy system depends on understanding what is happening now and what lies ahead.
The energy transition is changing how energy is generated, distributed and used. That makes it increasingly important to understand what is happening across the energy system and what is likely to happen next. Data and AI help make those insights available.
Challenges,
Energy demand is not constant, and supply is continually changing too. Organisations need to anticipate those changes while keeping the energy system reliable. That calls for information that goes beyond a snapshot in time.
How we support you,
We bring together information from different parts of the energy chain and make deviations immediately visible. This allows planners to adjust more quickly and coordinate processes more effectively when conditions change. These are a few examples of what is possible. The best solution always depends on the challenge and the organisation.
Data Science Lab at a glance
DSL in numbers,
40
Data experts
10+
Years of experience
200+
Projects completed
8,7
Client satisfaction
Get in touch with our experts,
Want to know what we can do for your organisation?
Younes Seghrouchni
Managing Consultant GenAI & Data StrategyE-mail info@datasciencelab.nl
Claudia Sulsters
Managing Consultant Data EngineeringE-mail info@datasciencelab.nl
Carmen Wolvius
Managing Consultant Data Science & AIE-mail info@datasciencelab.nl
Where we do that,
Latest blog posts,
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The organisations that do this,
Frequently asked questions,
AI can combine historical data with current information to reveal developments earlier. This provides a clearer view of future demand, network load and potential bottlenecks. It helps organisations plan ahead and prepare for change.
A successful application starts not with technology, but with a specific challenge. That might be predicting network load or planning maintenance more efficiently. Starting small and making the results visible creates a solid foundation for further applications.
AI supports employees with insights, but it does not take over responsibility. Decisions remain with people who understand the context. This way, AI contributes to a more reliable energy system without taking control out of human hands.
AI delivers value when organisations can better anticipate change. This could mean forecasting capacity, preventing disruptions or making better use of available resources. The greatest benefit comes when insights become part of daily operations.
AI only becomes valuable when employees can trust it and want to work with it. That is why we focus on ease of use, adoption and integration with existing processes. This makes AI part of the organisation rather than a standalone initiative.