2019 12 03 Grafik LIS Englisch

Product presentation: Further development of the analysis for charging infrastructure

Easy and targeted access to the best locations for electric charging stations with artificial intelligence

In the near future, we will present already established as well as new Geospin products. We explain why customers interested in accelerating the energy transition and new innovations can rely on Geospin’s analysis and how our tools work. This time we introduce: The evolution of our successful analysis for charging infrastructure. The following questions will be answered: What will the analysis for charging infrastructure look like next year? Who benefits from it and how does it work?

Electromobility is becoming more and more the focus of road users’ attention

It is essential to know the best and most economical charging locations so that energy suppliers benefit from the location of charging stations. The electrical charging stations should achieve the highest possible capacity utilization and be installed where they are needed by the users.

Benefit from state-of-the-art algorithms and an even more extensive database

The analysis for charging infrastructure has been part of Geospin’s portfolio since 2018. Geospin’s software evaluates over 800 geodata and historical usage data from charging stations with an intelligent algorithm. Since the analysis was established, we have continually expanded the database. In addition to various geographical influential data such as parking spaces, population density and vehicle data, the historical usage data of approximately 6,000 charging points with approximately 1.4 million charging processes in German cities as well as in rural areas are now included in the analysis. The combination of state-of-the-art deep learning methods with classical approaches of machine learning and an extensive database makes it possible to identify the locations where particularly high capacity utilization is expected for charging stations.

In the future, you will receive the forecast results in the form of an interactive user interface

So far, our customers have received the analysis results including the location recommendations in a PDF report. In the future, the evaluations of the analysis will be transformed into an interactive user interface with relevant points of interest. Energy suppliers can thus display the expected utilization of future electrical charging points in individual postcode areas as a heat map.

For whom is the analysis for charging infrastructure most benefitial?

Our analysis is an indispensable tool for organizations that want to expand their charging infrastructure and accelerate the energy transition. These organizations include municipal utilities, energy providers and energy-conscious municipalities. Our analysis is also a useful tool for providers of semi-public charging infrastructure. For example, retailers can find out which of their store locations have the greatest demand for electric charging stations.

2019-12-03 Grafik LIS englisch

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This is why it is worthwhile for you to rely on our analysis and our expertise

  • independent and objective analysis as a basis for your investment decisions
  • early securing of the most attractive locations for charging stations
  • increasing the profitability of your store network
  • alignment of your charging stations network in line with the needs of your customers
  • analyses also possible in regions, cities and municipalities without prior historical data
  • economic and ecological upgrade of the area

The analysis brings together “(…) data from municipal utilities with expertise from the fields of big data and machine learning in order to work through complex interrelationships, support decision-making and thus accelerate the transformation of energy systems”.
Eva-Maria Zauner, Innovation Thüga stock corporation

„The analysis for charging infrastructure offers an objective decision-support for the location selection of electric charging stations. As energy providers, we can thus provide charging stations where they are at the highest demand.”
Benjamin Hintz, Production Lead E-Mobility, WEMAG AG

“Working with Geospin is straightforward and fast. The analysis contributes to an acceleration of our processes.”
Jörg Fritz, Managing Director of Stadtwerke Homburg GmbH

These partners and many others trust our analysis for charging infrastructure

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Do you have any further questions? I am looking forward to your e-mail to mrattay@geospin.de!

 

Source of citation by Eva-Maria Zauner: Herrmann (2019): Machine Learning befeuert die Energiewende, in: Computerwoche, available under: https://www.computerwoche.de/a/machine-learning-befeuert-die-energiewende,3547041 (29.11.2019).

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