Smart City
The mobility of the future: The mobility concepts of the future will be as diverse as their inhabitants. Digital technologies not only make it possible to take stock, but can also predict the mobility behavior of the future and enable simulations. Congestion and particulate matter pollution can thus be reduced and more livable cities created.
Projects on the following measures have already been successfully implemented by urban energy in the past:
Real-time traffic and energy data - using sensors, cities collect real-time traffic data to identify congestion hotspots
AI-supported services for forecasting environmental, traffic, energy and CO2 data.
Visualization e.g. via intuitive dashboards or a Digital Twin for easy visualization of traffic and environmental data
TechInside
Portals
Intuitive dashboards for environmental, traffic, energy and CO2 data provide transparency and build trust
Internet of Things
IoT platforms, LoRaWAN networks or CoAP protocols (Constrained Application Protocol) for the technological implementation of a Digital Twin
Forecasting Services
AI forecasts for environmental, traffic, energy and CO2 data to derive measures to improve quality of life in cities and communities
reference
As part of the mobility concept Adlershof 2030+, WISTA together with urban energy built a central platform into which all relevant traffic and environmental data generated via a LoRaWAN network at the business location Berlin-Adlershof flow. The shared database and corresponding visualizations made it possible to implement measures to reduce congestion and environmental pollution at an early stage. The traffic and environmental data were also integrated into the central Berlin data portal Berlin Open Data and can be used for further applications.
reference
The city of Dortmund and the regional energy supplier DEW12 have developed an Energy DataHub together with urban energy to collect and visualize data from energy generation and consumption and to derive measures. In addition to the actual IoT platform (Energy DataHub), urban energy was tasked with the AI-powered services to predict energy behavior within the urban quarter. The goal of the project was to increase energy self-sufficiency, improve grid efficiency and reduce CO2.
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