High‑Precision Predictive Model Optimizes Smart Meter Rollout Strategy
SUMMARY
- Client
- ČEZ Distribuce
- Tech stack
- Python
For ČEZ Distribution, Profinit developed a high-precision predictive model to prioritize smart meter rollout across 4M consumption points. Using an agile modeling approach and processing billions of data rows, we achieved 98.7% AUC accuracy, enabling data-driven deployment based on consumption.
Results
Project Background
Striving to develop and improve services, ČEZ Distribuce planned to gradually install smart meters in the electricity distribution network. Priority was to be given to locations with a higher proportion of consumption points and higher levels of expected consumption.
To take on the project, the customer, ČEZ ICT Services, domain expertise and data sources work, collaborated with Profinit, which provided the methodological and modeling part.
Challenge
Predictive modeling is generally a demanding discipline that requires a professional approach and the selection of appropriate methods.
This particular task required processing forecasts for almost 4 million consumption points with very different characteristics. Just analyzing historical records meant working with data sets with billions of rows.
Solution & Results
Using Profinit’s project methodology, we managed to set up an agile project plan from the beginning of the project. This enabled us to develop a minimal reference model early and gradually refine it throughout the project. We thus avoided problems with missing deadlines or output quality.
The project was developed in the form of interactive IPython notebooks, so that the executable code of the models was organically linked to the analytical data processing and documentation of the solution. This eliminated problems with insufficient documentation or inconsistency of the project outputs.