The problem
Industrial energy decisions connect price signals with operational requirements. A proposed schedule has to make sense alongside equipment constraints and observed system behavior.
My work
I co-develop UtiliQ as part of the founding team, working across product and data. The early-stage concept focuses on energy optimization and test-bench scheduling for manufacturers of generators, engines, and power units.
I ran customer discovery with industrial stakeholders and translated the findings into an MVP scope: data integration, asset monitoring, generation and consumption forecasting, and scheduling under grid and due-date constraints. The project was developed through the REACH EUREGIO Start-up Center’s Headventures program in Münster.
System decisions
The conceptual illustration above separates a price signal from a scheduling window. It shows the kind of relationship a scheduling interface needs to explain: when energy is less expensive, and when an operation is actually permitted.
The product question is how to connect monitoring and forecasting to a feasible schedule. The MVP scope brings sensor, MQTT, ERP, and spreadsheet inputs into that decision process. The public-price Chronos-2 study is a separate demonstration of forecasting evaluation; it is not evidence of a deployed industrial forecasting service.
Evaluation
A useful evaluation needs to distinguish schedule feasibility, economic objectives, and the reliability of the surrounding software. Customer discovery and MVP development establish the problem and product scope; they do not establish realized savings. No production savings are claimed here.
Limitations
The chart uses synthetic values. Customer identities, plant data, internal architecture, and proprietary screenshots are excluded. UtiliQ remains an early-stage concept; this overview does not imply a completed customer deployment.