Digital Twin Environmental Control for Predictive Energy Optimization
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Solution Overview
Problem
Traditional environmental control systems in buildings are inefficient due to their reliance on reactive technologies and inability to account for variations in building geometry, layout, and occupancy, leading to higher energy usage and component wear.
Innovation Solution
A system utilizing digital twin modeling and machine learning models to predict environmental conditions, occupancy, and power consumption, enabling proactive optimization of energy use by generating real-time optimization instructions for environmental control devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If traditional reactive technologies (e.g., thermostat) are used to control environmental conditions, then the system is simple to implement, but energy usage increases significantly
Solution Approach 1:
The system performs preliminary actions by predicting future environmental conditions and occupancy patterns using machine learning models. It proactively adjusts environmental control devices before actual changes occur, such as pre-cooling zones before occupancy is predicted or pre-heating during unoccupied periods, thereby reducing overall energy consumption while maintaining comfort standards.
Solution Approach 2:
The system creates a digital twin (virtual copy) of the physical building environment that mirrors its geometry, layout, and environmental parameters. This digital replica allows the system to simulate and optimize energy consumption scenarios without affecting the actual building, enabling energy-efficient control strategies to be developed and implemented based on virtual testing.
2Measurement precision
If traditional environmental control systems are used, then the system structure is simple, but the system cannot calculate and compensate for variations created by building geometry, layout, or occupancy
Solution Approach 1:
The system applies local quality by dividing the building into multiple zones with distinct environmental characteristics based on geometry, layout, and occupancy patterns. Each zone receives customized environmental control instructions tailored to its specific requirements, such as different temperature setpoints or ventilation rates, rather than applying uniform control across the entire building.
Solution Approach 2:
The digital twin creates a precise virtual replica of the building's geometry and layout, allowing the system to accurately model and compensate for spatial variations in environmental conditions. This virtual copy enables the system to calculate and adjust for differences in solar exposure, thermal mass, and airflow patterns specific to each zone's physical characteristics.
3Loss of time
If traditional reactive control is used, then the response time is fast, but the system cannot predict future environmental conditions or occupancy patterns
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict future environmental conditions and occupancy patterns ahead of time. It proactively adjusts environmental control devices based on these predictions, such as pre-cooling zones before predicted occupancy or pre-heating during unoccupied periods, thereby optimizing energy usage before actual conditions change.
Solution Approach 2:
The system dynamically adapts its control strategy by continuously updating predictions based on real-time sensor data and changing conditions. The machine learning models learn from historical and current data to improve future predictions, allowing the system to flexibly respond to varying occupancy patterns, weather conditions, and building usage while optimizing energy consumption over time.
Data Source
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AI summary
The system may utilize input data sensors to populate a digital twin of an environment. The system may predict, using machine learning models, a duration required to change an observed environmental condition to a target environmental condition using one or more environmental control devices and one or more occupancy intervals of one or more occupants of the environment. The system may calculate, using the digital twin, an estimated power consumption at the target environmental condition and optimization instructions for the environmental control devices. The system may additionally estimate, using a third machine learning model, an environment optimization parameter based on the one or more optimization instructions and the input data. The system may dynamically generate a graphical user interface comprising a graphical representation of the environment and the environment optimization parameter.