AI-Charge Energy Management
Artificial intelligence as key technology for charging of electric vehicles.
The charging and energy management services by AI-Charge Technologies GmbH enabling intelligent solutions for real-time prediction and optimization of sustainable energy and mobility systems.
We are supporting vehicle owners as well as public and commercial institutions in operation of electric vehicles in order to achieve the best possible ecological and economic benefits.
Our approach is based on the idea that individual energy flows are predictable. Optimal energy consumption and transfer scenarios will controlled with the help of self-learning systems. Various energy resources such as renewable or stored energies will be considered.
The underlying IoT platform provides always/everywhere connectivity and intelligence for green and sustainable solutions. Thus smart components can be integrated quickly and easily for the optimal control of energy flows.
We deliver a cross-platform app and intelligent charging points customized for your e-mobility challenge.
#Energy #Efficient #Emotion
Solar-Mobilität
Electric Vehicle interconnected to renewable energy sharing networks
Micro Grid
Energy concepts for smart home to scaled smart cities
Charging Eco-System
Smart wallbox and smart charging
Power Grid
Concepts for load and reactive power controls
Energy Efficiency
Cloud-based energy analysis and real-time optimization maximizing renewable resource efficiency
Using new methods like artificial intelligence and increasing connectivity introduce essential challenges for future energy management architectures.
IoT networking enable the systematic recording and forecasting of energy flows in plants, buildings or vehicles and thus creates an important basis for maximizing energy efficiency and reliability as well as lower asset costs and CO2 emissions.
Particularly in the case of self-powered systems based on renewable energy sources, a high potential return can be achieved through the maximum use of self-generated electricity and the lowest possible purchase of conventional electricity from the power grid.
Our approach to maximizing energy efficiency is based on the intelligence of self-learning systems. The basis is formed by artificial neural networks predicting the best energy-optimal scenario from a variety of information sources.
In addition, prosumers receive a detailed energy report with information, e.g. on the degree of self-sufficiency achieved or on operating economics.
#Energy #Efficient #Emotion
Scaling Up Smart Cities
Understanding of how the application of disruptive technology can solve urban challenges
Smart Sensing
Climate & Environment Sensing and Evaluation
Smart Mobility
Infrastructure and intermodal transport
Smart Energy
Building, environment and grid integration
