LPDG E-Mobility

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The increasing demand for e-mobility requires a solid data basis and advanced analyses to optimize charging infrastructures and their tariff structures. Future expansion and investment in the charging infrastructure has to be supported by deep analysis of the different charge points and charging behaviours.

Data from different backend providers has to be automatically extracted and integrated into a unified data model for analysis. This enables comparative analysis for the entire charging infrastructure and the application of machine learning and predictive models.

The use of advanced analytics, machine learning and artificial intelligence enables insights into charging behaviour and charging infrastructure utilisation. Forecast models predicts the future energy demand on different forecast horizons. However, the prediction models needs to be able to deal with time-series of variable length and missing data.


The LPDG Analytics Platform offers a wide range of possibilities for applying advanced analytics and machine learning to e-mobility and charging infrastructure data.


Advanced analysis, machine learning and prediction combined with progressive data visualisation transforms charge-log data into insights.

Incorporating geospatial data into the analysis enables charging data to be evaluated together with the positions of the charging stations.

The use of machine learning methods enables the prediction of energy consumption and the future utilization of the charging infrastructure. The ability to combine customer tariff data with charging data eliminates the need for time-consuming manual reporting solutions.

LPDG E-Mobility Insights turns charging data into insights. Standard methods for charge infrastructure analysis brings fast results. Customer-specific adaptations and extensions offers maximum flexibility

Renato Gonzaga

Senior Data Scientist Big Data + Data Science

"Big Data and Data Science lift the value of data ensures competitiveness, decision-making and digital transformation."