High‑Precision Predictive Model Optimizes Smart Meter Rollout Strategy
Predictive consumption model helps a leading electricity distributor prioritize smart meter rollout across 4M metering points with high prediction accuracy.
Rather than relying on assumptions, the distributor gained a statistically sound basis for planning. The predictive model turned complex consumption data into a reliable input for decision making, enabling informed prioritization of smart meter deployment and more effective investment planning.
CEZ Distribuce, the largest electricity distributor in the Czech Republic, member of CEZ Group, 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, CEZ ICT Services, domain expertise and data sources work, collaborated with Profinit, which provided the methodological and modeling part.
Challenges
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 made it possible to put together a minimal solution in the form of a reference model very early on and gradually improve the solution during 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.
The project also included a very thorough data quality analysis and several iterations of the data release process until the modeling suite was finalized in its satisfactory form.
The entire analysis and modeling process was carried out in close cooperation with CEZ Distribuce domain experts, who were kept continuously informed of the project's status and future direction through regular status updates. This approach contributed to CEZ evaluating the project as very successful and beneficial.
We developed an annual predictive model of the consumption point in a relatively short time, and with an accuracy exceeding 98.7% AUC for classifying the consumption point into a higher or lower consumption category according to the selected threshold (6MHh/year).
Key Facts & Achievements
- Predictive model of annual consumption for 4 million consumption points
- Prediction accuracy exceeding 98.7% AUC
- The model provides data for determining regions suitable for priority deployment of smart meters
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