Myrspoven AI
Published by
Myrspoven AB
Features
BMS Optimization
AI algorithms and machine learning are applied to all data retrieved per building. Each building has a unique AI. The result is a virtual building model used to predict future energy use and indoor climate. The models are trained every night and are constantly improving.
![BMS Optimization]()
KEY TAKEAWAYS
-
Average electrical saving of 15% and a thermal saving of 10-25%.
-
Every building gets its own AI model, which being updated every week.
-
The optimizer process runs every 15min with new BMS-data sent.
-
A new building takes 8h to integrate and run.
-
A fall-back program reset BMS set-points to default in case of failure or planned shutdown.
Improves the indoor climate using Myrspoven AI
Make your tenants happy by our customizable algorithms that ensure indoor temperature is kept into a predefine range while taking into consideration outdoor temperature, building configuration, energy tariffs and other factors.
![Improves the indoor climate using Myrspoven AI]()
Best indoor climate
-
The customer is able define the indoor climate. The intelligent AI-based system uses data collected onsite to teach itself how to adjust heat, cooling and ventilation depending on prevailing external conditions (such as weather or wind) and on how the specific buildings are designed, used and configured.
Result
-
Right indoor climate achieved at the lowest possible energy usage and cost, optimized autonomously every 15 min.
Save Energy using Myrspoven AI
Myrspoven’s solution enables forecasting of both energy costs and climate, which enables control adjustments that takes into account energy tariffs, flow and return penalty fees, price cuts and daily variations. The model is upgraded and optimized every day as it learns from the data collected.
![Save Energy using Myrspoven AI]()
Easy to Implement
Myrspoven’s team has developed a scalable system that, with the help of modern AI, enables a continuous and autonomous optimization of complex buildings. The core of the system is based on scalability and flexibility and is independent of the manufacturer in terms of both sensors and control systems.
Big Data - Unique Insights
-
All data collected in real time is saved into a database and uploaded into the building’s digital twin. This data is combined with other datasets generated from different energy providers, weather forecasting system, etc to generate models. About once per hour, new control signals are sent back via the connector to the BMS based on these predictions.
BMS Optimization
AI algorithms and machine learning are applied to all data retrieved per building. Each building has a unique AI. The result is a virtual building model used to predict future energy use and indoor climate. The models are trained every night and are constantly improving.
KEY TAKEAWAYS
- Average electrical saving of 15% and a thermal saving of 10-25%.
- Every building gets its own AI model, which being updated every week.
- The optimizer process runs every 15min with new BMS-data sent.
- A new building takes 8h to integrate and run.
- A fall-back program reset BMS set-points to default in case of failure or planned shutdown.
Improves the indoor climate using Myrspoven AI
Make your tenants happy by our customizable algorithms that ensure indoor temperature is kept into a predefine range while taking into consideration outdoor temperature, building configuration, energy tariffs and other factors.
Best indoor climate
- The customer is able define the indoor climate. The intelligent AI-based system uses data collected onsite to teach itself how to adjust heat, cooling and ventilation depending on prevailing external conditions (such as weather or wind) and on how the specific buildings are designed, used and configured.
Result
- Right indoor climate achieved at the lowest possible energy usage and cost, optimized autonomously every 15 min.
Save Energy using Myrspoven AI
Myrspoven’s solution enables forecasting of both energy costs and climate, which enables control adjustments that takes into account energy tariffs, flow and return penalty fees, price cuts and daily variations. The model is upgraded and optimized every day as it learns from the data collected.
Easy to Implement
Myrspoven’s team has developed a scalable system that, with the help of modern AI, enables a continuous and autonomous optimization of complex buildings. The core of the system is based on scalability and flexibility and is independent of the manufacturer in terms of both sensors and control systems.
Big Data - Unique Insights
- All data collected in real time is saved into a database and uploaded into the building’s digital twin. This data is combined with other datasets generated from different energy providers, weather forecasting system, etc to generate models. About once per hour, new control signals are sent back via the connector to the BMS based on these predictions.
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