AI Building Control Agents for Adaptive Subsystem Coordination
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Solution Overview
Problem
Current building management systems (BMS) lack a unified, intelligent framework to dynamically react to changing situational data across disparate building systems, leading to inefficiencies and potential safety issues due to predefined scripted operations.
Innovation Solution
A BMS with artificial intelligence capabilities, incorporating data collectors, a learning engine, and cognitive agents that correlate data streams from various subsystems to identify building states and generate control decisions, enabling dynamic and adaptive management of building conditions, including emergency responses like fire control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a unified AI-driven control framework is implemented, then building safety and operational efficiency are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The system segments building operations into multiple cognitive agents, each responsible for specific subsystems (HVAC, lighting, security, etc.). Each agent independently processes data and makes control decisions for its domain, reducing overall system complexity while maintaining unified AI-driven coordination through the learning engine.
Solution Approach 2:
The learning engine acts as an intermediary between data collectors and cognitive agents, synthesizing data from multiple sources and providing coordinated control decisions. This intermediary layer manages the complexity of integrating multiple subsystems while enabling safe and efficient building operations.
2Adaptability or versatility
If predefined scripted operations are used in BMS, then system implementation is simplified, but adaptability to changing situational data deteriorates
Solution Approach 1:
The system transitions from static predefined scripts to dynamic cognitive agents that continuously adapt their behavior based on real-time data from learning engines. Each cognitive agent dynamically adjusts control decisions according to changing building conditions, occupancy patterns, and environmental factors, enabling high adaptability while maintaining manageable complexity through modular agent architecture.
Solution Approach 2:
Cognitive agents autonomously make control decisions based on data processed by learning engines, without requiring predefined scripted operations for every scenario. The agents self-adjust to changing conditions by interpreting synthesized data and generating appropriate control actions, reducing the need for complex pre-programming while maintaining adaptability.
Data Source
AI summary
A building management system with artificial intelligence based control of a building includes data collectors are configured to receive data and generate data streams for subsystems of the building. The system includes a learning engine configured to identify a building state of the building by correlating data of the data streams for the subsystems and provide the identified building state to cognitive agents. The system includes the cognitive agents, each of the cognitive agents configured to receive the identified building state from the learning engine, generate a control decision based on the received building state, and operate at least one of the plurality of subsystems of the building to control a physical condition of the building based on the control decision.