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Building Digital Twin Modeling for Indoor Climate Management
This technology offer presents a simplified development of smart analytics to control indoor environmental quality for building heating, ventilation, and air conditioning (HVAC) systems. It is a combination of digital twin modeling method that defines the relationship between different HVAC system devices & sensors and machine learning algorithm that optimizes HVAC operations according to real-time data of the existing equipment, sensor devices, and projected outdoor weather on top of the model.
The system collects data from various sensors and networks in a building, such as the battery-powered-wireless indoor air quality (IAQ) sensor, temperature sensor, and humidity sensor network, and the gateway that are connected to the building management system (BMS) that fetch data from each HVAC equipment point. Collected data is analyzed by the designed model. The model is developed based on the real-time digital twin of the building and the machine learning algorithm which learns the building characteristics and performance. Then, it evaluates the best outputs that optimize HVAC system control according to IAQ, the thermal comfort level, and the cooling load of the system. The system is fully customizable and configurable based on the user's requirements.
The technology owner is currently looking for opportunity to collaborate with technology companies which are operating in energy monitoring, green building, smart building, and IoT verticals.
Technology Features, Specifications and Advantages
The technology standardizes semantic descriptions of the physical, logical, and virtual assets in buildings and their relationships. Furthermore, it builds a machine learning algorithm on top of these relationships that uses historical and real-time data of points all over the building, and projected outdoor weather to automate and optimize the HVAC control process.
Typically, BMS runs with predefined scenarios to operate HVAC systems. Hence, it cannot reflect dynamic situations or work according to the real-time performance of equipment. Real efficiency, however, comes not just from tracking but from the ability to predict and respond. Therefore, this technology was developed to solve this inadequacy.
Key features of the technology:
- Simple modeling tool to create IoT digital twin of a building
- Machine learning-based algorithm that estimates real-time zone HVAC equipment performance, energy consumption pattern, zone indoor air quality & occupant’s thermal comfort
- Two-level control optimization set-point control - optimization of fan coil unit (FCU) and thermostats
- Cooling load control- Chiller & AHU scheduling optimization
- Wireless- battery powered indoor environmental sensors and easy to install energy monitoring devices to validate digital twin model & smart algorithm efficiency
- Vendor-agnostic control of HVAC systems over common communication protocols (Modbus, BACnet, etc.) via using the gateway which will be installed to the building
- Web & Mobile visualization and analytics tool
The system is flexibly designed and can be implemented both in old and new buildings in which energy efficiency and occupancy comfort are priorities for building management.
Potential applications include
- Commercial, residential, and industrial buildings.
- Public area, such as park, carpark and walking pavement.
The technology provider is also looking for collaboration to improve capabilities:
- Low power gas sensing technologies
- Next generation batteries for IoT devices
- Low cost and rapidly deployable control tools for traditional HVAC equipment
- Gathering data from BIM (Building Information Modelling) workflows
The technology creates two-sided value for humans and the building itself.
It prioritizes minimizing HVAC-related energy consumption by 15-30%, increasing the occupants' wellness and the building's air quality.
This specific solution is burden-free due to the cable-free and end-to-end concept. It is instantly deployable, flexible to be integrated and customized, rich offerings with data analytics, reporting, prediction, and control capabilities.