Reinforcement Learning Building Control for Adaptive Setpoints
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Solution Overview
Problem
Building automation systems face challenges in efficiently regulating environmental factors like temperature and humidity due to external factors, requiring frequent setpoint changes and maintenance, and existing control methods lack predictive capabilities to optimize energy use and comfort levels.
Innovation Solution
An automation system utilizing a first artificial intelligence model trained on operational data from a specific business vertical category, which includes reinforcement learning models, to generate decisions for controlling equipment and performing automated actions, such as maintenance, to optimize physical conditions within a building.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional building automation systems use fixed setpoints for environmental control, then system operation is simple, but the system cannot adapt to external factors requiring frequent manual adjustments
Solution Approach 1:
The patent implements dynamic setpoint adjustment by training reinforcement learning models on historical operational data to generate time-varying setpoints that adapt to external factors. The system transitions from static fixed setpoints to dynamic adaptive setpoints that automatically respond to changing environmental conditions, occupancy patterns, and external disturbances without manual intervention.
Solution Approach 2:
The system performs preliminary actions by training AI models on historical data beforehand to predict optimal setpoints before external factors change. The reinforcement learning model learns from past operational patterns and proactively adjusts setpoints in anticipation of future conditions, reducing the need for reactive manual adjustments.
2Productivity
If traditional systems rely on manual setpoint changes and maintenance, then system structure is simple, but productivity and energy efficiency are reduced
Solution Approach 1:
The system implements self-service by enabling the building automation system to automatically optimize its own operation through reinforcement learning. The AI model autonomously adjusts setpoints based on learned patterns from historical data, eliminating the need for manual optimization and enabling the system to self-improve energy efficiency over time without human intervention.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring operational data and using it to train and refine the reinforcement learning model. The model learns from past performance outcomes and adjusts future setpoint decisions accordingly, creating a closed-loop system that continuously improves energy efficiency through data-driven feedback.
3Reliability
If frequent manual interventions are performed to maintain comfort levels, then system control is direct, but loss of time and operational efficiency increase
Solution Approach 1:
The patent replaces manual mechanical adjustment operations with intelligent software-based control. The reinforcement learning model automatically generates and implements setpoint adjustments that maintain comfort levels, substituting human operators with an AI-driven control system that responds instantaneously to changing conditions without requiring manual time investment.
4Use of energy by moving object
If existing control methods lack predictive capabilities, then system design is straightforward, but energy consumption optimization is limited
Solution Approach 1:
The system performs preliminary energy optimization actions by training the reinforcement learning model on historical operational data to predict future energy consumption patterns. The model learns optimal setpoint strategies in advance that minimize energy usage while maintaining comfort, enabling proactive energy management rather than reactive control.
Solution Approach 2:
The system optimizes energy consumption by dynamically changing operational parameters (setpoints) based on predictions from the reinforcement learning model. The AI model identifies optimal parameter adjustments that reduce energy consumption during different operating conditions, transforming the control approach from fixed parameters to dynamically optimized parameters.
Data Source
AI summary
An automation system to affect one or more physical conditions of a site is disclosed. The automation system provides an artificial intelligence model used to generate decisions regarding the equipment operated by the automation system. The decisions are used to affect a physical condition of the building. The artificial intelligence model used to generate the decisions is provided based on a business vertical category associated with the automation system. The artificial intelligence model is trained on operational data of a previous time period and the operational data is associated with the business vertical category associated with the automation system.