Building control system with constraint generation using artificial intelligence model
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Solution Overview
Problem
Current building control systems lack an efficient method to optimize energy consumption by HVAC equipment, as they fail to effectively generate and enforce constraints on temperature setpoints, leading to suboptimal energy usage and increased costs.
Innovation Solution
A predictive building control system utilizing a neural network model to generate and enforce constraints on temperature setpoints for HVAC equipment, which includes a constraint generator that classifies and trains on performance scores to optimize energy usage based on factors like outdoor temperature, occupancy, and valve positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional building control systems are used without AI-based constraint generation, then the system complexity remains low, but energy consumption optimization is insufficient
Solution Approach 1:
A neural network model is introduced as an intermediary component between the optimization controller and the HVAC equipment. The neural network generates temperature constraints based on historical data and patterns, which then guide the optimization controller's setpoint adjustments. This intermediary enables sophisticated energy optimization without requiring the entire control system to become exponentially more complex.
Solution Approach 2:
The neural network model continuously learns from historical building data and automatically refines its constraint generation capabilities. The system performs self-training by analyzing past performance and adjusting its internal parameters, enabling it to progressively improve energy optimization without requiring manual reconfiguration or external intervention for each improvement.
2Productivity
If dynamic constraint generation using neural networks is implemented, then energy optimization improves, but computational requirements and processing time increase
Solution Approach 1:
The neural network model pre-calculates and generates temperature constraints in advance based on historical patterns and predicted conditions. By preparing constraint guidelines beforehand rather than computing optimal setpoints in real-time, the system reduces computational burden during critical control moments while maintaining high optimization effectiveness.
3Adaptability or versatility
If manual adjustment of setpoints is allowed, then operational flexibility improves, but constraint effectiveness deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where manual setpoint adjustments are monitored and used to retrain the neural network model. When operators manually adjust setpoints, the system learns from these actions and updates its constraint generation algorithms, ensuring that future automated constraints reflect actual operational requirements and maintain effectiveness despite manual interventions.
Data Source
AI summary
A building control system includes one or more processors and one or more non-transitory computer-readable media storing instructions. When executed by the one or more processors, the instructions cause the one or more processors to perform operations including using an artificial intelligence model to adjust a threshold value of a constraint based on user input provided via one or more user devices during a first time period. The user input indicates user satisfaction with an environmental condition of a building space during the first time period. The operations include using the threshold value of the constraint to operate equipment that affect the environmental condition of the building space during a second time period subsequent to the first time period.