Digital Twin Event Enrichment for Holistic Building Control
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Solution Overview
Problem
Current building management systems lack dynamic, scalable, and adjustable solutions for holistic management of building operations, as they operate independently without knowledge of the building's overall conditions, leading to inefficiencies in energy usage and occupant comfort.
Innovation Solution
A cloud-based building management system that ingests event information from building systems and external sources, enriches it using digital twins, and generates predicted parameters to inform control decisions, optimizing energy usage and occupant comfort through machine learning and sustainability models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If discrete predefined controlling systems operate each subsystem individually, then each subsystem can be controlled independently, but holistic management of the building is not achieved and energy efficiency is reduced
Solution Approach 1:
The patent merges multiple discrete building subsystems (HVAC, lighting, security, etc.) into a unified building management system that operates them collectively. The system integrates data from various subsystems and applies coordinated control strategies to achieve holistic management while optimizing energy efficiency across the entire building rather than treating each subsystem in isolation.
Solution Approach 2:
The building management system is designed as a universal platform that can control and manage multiple different types of building subsystems through a single integrated interface. The system provides multi-functional capabilities including monitoring, control, optimization, and analytics across diverse subsystems, enabling holistic management without requiring separate discrete control systems for each subsystem.
2Adaptability or versatility
If predefined control systems are used, then implementation is straightforward, but dynamic and scalable solutions cannot be provided
Solution Approach 1:
The control system transitions from static predefined rules to dynamic adaptive control that can adjust in real-time based on building conditions, occupancy patterns, and external factors. The system learns from historical data and continuously optimizes control strategies, enabling dynamic and scalable solutions that adapt to changing building requirements while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The system enables flexible adjustment of control parameters and operational settings without requiring system reconfiguration or hardware changes. By allowing dynamic modification of control parameters such as temperature setpoints, scheduling profiles, and energy optimization thresholds, the system provides adaptable and scalable solutions while maintaining a stable underlying platform that manages complexity.
3Productivity
If real-time data processing is implemented, then predictive analytics can be generated, but system complexity increases
Solution Approach 1:
The system performs preliminary data processing and feature extraction in real-time as data is collected from building subsystems, preparing data for predictive analytics before it needs to be analyzed. By pre-processing data streams, filtering relevant information, and organizing data structures in advance, the system enables sophisticated predictive analytics without overwhelming the processing infrastructure with raw data complexity.
Solution Approach 2:
The patent introduces intermediate data layers and processing modules that act as mediators between raw sensor data and predictive analytics algorithms. These intermediary components aggregate, normalize, and structure data from multiple sources before feeding it to predictive models, reducing the complexity burden on the analytics engine while maintaining the ability to generate actionable predictive insights.
Data Source
AI summary
A building management system for a building including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to ingest event information from at least one of a building system or an external computing system, enrich the event information based on a digital twin associated with the event information, wherein enriching the event information includes adding contextual information to the event information based on the digital twin to generate enriched event information, generate a predicted parameter that will result from a control decision for operating at least one of the building system or a different building system based on the enriched event information, and modify the control decision based on the predicted parameter.