We are turning HW into intelligent energy devices
02 OUR TECHNOLOGIES
Edge Energy
Intelligence Technologies
We are developing technologies to embed energy intelligence directly into individual devices, where it collects, analyses, makes predictions and learns.
Energy Behavior Prediction
- Our EEI predicts energy behaviour of individual prosumers, detect consumption patterns, predicts responses to tariff switching and grid flexibility requests.
Self-Learning and Self-Maintaining Capabilities
- Our EEI provides autonomous machine learning directly within the device, requiring no cloud connection or data scientists for setup or future maintenance.
Full utilization of all on-site measurements
- We use all values measured in 1 sec. intervals. This makes analyses and predictions much more accurate.
Smart Device Component
- We integrate EEI as a component (either hardware + software or software-only) into smart devices such as smart meters, EV chargers, batteries, and PV inverters.
03 GENERAL CONTRIBUTION
Explore the Benefits of Our Technology
More accurate prediction due to higher data granularity - 1x per second
Lower data transfer volume due to edge computing
Faster with LML (Local Machine Learning)
Higher data security thanks to local processing
04 APPLICATION AREAS
Edge Energy Intelligence
Brings Energy Excellence
Grid Management
We are upgrading current grid devices with edge energy intelligence. It can be integrated to meters, submeters, energy monitors, quality meters, PLC controllers or RTUs. Then data driven optimal short and mid term grid investment planning is available. Grid limits and available flexibility of prosumers and grid zones helps to understand current usage of the grid. Precise predictions are helping to successfully solve challenges as decarbonization, integration of renewable sources, new flexibility markets, energy communities and energy sharing.
Energy Management
Energy controllers are upgraded to provide short term precise prediction of energy flows through metered point. It provides precise predictions for energy controllers. Why? To decrease energy costs, reveal and monetize own flexibility, maximize usage of own renewable resources, minimize carbon own footprint.
Energy Communities
Upgraded community energy controllers will autonomously learn behaviour of individual community members. It boosts effectivity of community energy flows. Result is effective utilization of renewable community energy sources, monetization of community flexibility, decreasing energy costs and minimizing carbon footprint.
Photovoltaics
Precise short term prediction with 30 second time granularity for next hour is not available from nowadays weather cloud services. It needs to handle prediction of passing cloud movements shading solar panels. Our combination of edge machine learning and sky image processing is able to do that. Clever utilization of solar energy is not possible without precise predictions.
Electromobility
Charging is energy consumption pattern. We are enhancing PLC charging station controllers with functionality allowing to predict charging energy and power required by EV. It is increasing effectivity of maximal charging capacity and provides precise short term predictions of charging demands.
Flexibility Services
Flexibility aggregators need to deeply understand their prosumers flexibility. We are enhancing energy controllers and submeters with ability to discover, detect and predict available prosumer flexibility.
Grid Management
We are upgrading current grid devices with edge energy intelligence. It can be integrated to meters, submeters, energy monitors, quality meters, PLC controllers or RTUs. Then data driven optimal short and mid term grid investment planning is available. Grid limits and available flexibility of prosumers and grid zones helps to understand current usage of the grid. Precise predictions are helping to successfully solve challenges as decarbonization, integration of renewable sources, new flexibility markets, energy communities and energy sharing.
Energy Management
Energy controllers are upgraded to provide short term precise prediction of energy flows through metered point. It provides precise predictions for energy controllers. Why? To decrease energy costs, reveal and monetize own flexibility, maximize usage of own renewable resources, minimize carbon own footprint.
Energy Communities
Upgraded community energy controllers will autonomously learn behaviour of individual community members. It boosts effectivity of community energy flows. Result is effective utilization of renewable community energy sources, monetization of community flexibility, decreasing energy costs and minimizing carbon footprint.
Photovoltaics
Precise short term prediction with 30 second time granularity for next hour is not available from nowadays weather cloud services. It needs to handle prediction of passing cloud movements shading solar panels. Our combination of edge machine learning and sky image processing is able to do that. Clever utilization of solar energy is not possible without precise predictions.
Electromobility
Charging is energy consumption pattern. We are enhancing PLC charging station controllers with functionality allowing to predict charging energy and power required by EV. It is increasing effectivity of maximal charging capacity and provides precise short term predictions of charging demands.
Flexibility Services
Flexibility aggregators need to deeply understand their prosumers flexibility. We are enhancing energy controllers and submeters with ability to discover, detect and predict available prosumer flexibility.