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 will happen next.
The energy transition is changing how energy is generated, distributed and used. This 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 these shifts while keeping the energy system reliable. That requires information that goes beyond a snapshot of the current situation.
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 better coordinate processes when circumstances change. These are a few examples of what is possible. The right 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
Carmen Wolvius
Managing Consultant Data Science & AIE-mail info@datasciencelab.nl
Stef Stoelwinder
Business DevelopmentE-mail info@datasciencelab.nl
Where we do that,
Latest blog posts,
Read our latest insights here.
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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 gives organisations better insight into future demand, grid load and potential bottlenecks. It helps them plan ahead and prepare for change.
A successful application starts not with technology, but with a specific challenge. That could be predicting grid 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 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 away from them.
AI delivers value when organisations can better anticipate change. That might mean predicting capacity needs, preventing disruptions or making smarter use of available resources. The greatest benefit comes when insights become part of day-to-day 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 way, AI does not remain a standalone initiative but becomes part of the organisation.