Multi-Space Learning Building Control
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Solution Overview
Problem
Traditional HVAC control methods fail to account for complex thermal interactions across multiple zones in a building, lack adaptability to changing conditions, and struggle with high dimensionality and nonlinear behavior, leading to inefficiencies in energy consumption and greenhouse gas emissions.
Innovation Solution
An AI-powered system using graph learning and reinforcement learning constructs a thermal model of a building, encoding zone-specific features and optimizing HVAC setpoints to minimize energy consumption while ensuring thermal comfort, with transfer learning for scalability across diverse environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional rule-based or model-predictive control methods are used for HVAC systems, then the control logic is simple and easy to implement, but the system fails to account for complex thermal interactions across multiple zones and lacks adaptability to changing conditions
Solution Approach 1:
The patent replaces traditional mechanical control systems (rule-based controllers, model-predictive control algorithms) with an AI-based neural network system. The neural network learns complex thermal interactions and occupancy patterns from historical data, enabling adaptive control without requiring explicit programming of control rules. This substitution allows the system to handle nonlinearity and multi-zone coordination while maintaining computational efficiency through transfer learning.
Solution Approach 2:
The system dynamically adjusts HVAC setpoints based on learned patterns from historical data and real-time sensor inputs. The neural network processes multiple parameters including temperature, humidity, occupancy, and weather conditions to optimize control decisions. Transfer learning enables the system to adapt parameters across different buildings and zones by leveraging knowledge from previously trained models.
2Productivity
If white-box physical models such as EnergyPlus or simplified grey-box models are used to describe thermal dynamics, then the thermal behavior can be described in detail, but the models are difficult to scale or adapt in real-time
Solution Approach 1:
The patent uses transfer learning to copy knowledge from previously trained neural network models to new buildings or zones. Instead of training complex physical models from scratch for each building, the system transfers learned thermal dynamics and control strategies across different environments. This approach enables rapid deployment and real-time adaptation without requiring extensive building-specific calibration or retraining.
Solution Approach 2:
The system replaces computationally intensive white-box physical models (EnergyPlus) with a data-driven neural network approach. The neural network captures thermal dynamics through learned relationships from historical data, eliminating the need for complex energy balance calculations and building envelope simulations. This substitution achieves comparable or superior accuracy while enabling real-time control and easy scalability across multiple buildings.
3Adaptability or versatility
If rule-based or model-predictive control is used, then the control strategy is straightforward to implement, but the system struggles with high dimensionality, nonlinear behavior, and multi-zone coordination
Solution Approach 1:
The patent replaces simple rule-based control logic with an AI-based neural network system that automatically learns complex control strategies from historical data. The neural network handles high-dimensional inputs (multiple zones, weather conditions, occupancy patterns) and nonlinear thermal dynamics through its layered architecture and activation functions. This substitution eliminates the need for manual tuning of control rules while achieving superior performance in multi-zone coordination.
Solution Approach 2:
The neural network system serves multiple functions simultaneously: it predicts thermal dynamics, optimizes setpoints, coordinates multi-zone control, and adapts to changing conditions. The same model architecture handles different building types, zones, and environmental conditions through transfer learning, providing a universal solution that replaces multiple specialized control systems.
4Loss of energy
If traditional HVAC control methods are used, then the system operation is simple, but energy consumption and greenhouse gas emissions are significantly high
Solution Approach 1:
The system implements closed-loop feedback control using real-time sensor data from multiple zones (temperature, humidity, occupancy) and weather stations. The neural network continuously monitors system performance and adjusts setpoints based on actual thermal responses and energy consumption patterns. This feedback mechanism enables the system to learn from past operations and progressively optimize energy efficiency while reducing greenhouse gas emissions.
Solution Approach 2:
The patent replaces traditional energy-intensive HVAC control with an AI-optimized control system that learns efficient operating strategies from historical data. The neural network identifies patterns in energy consumption and thermal responses to optimize setpoints, reducing unnecessary heating and cooling while maintaining comfort. This substitution achieves significant energy savings by eliminating wasteful control actions and coordinating multi-zone operations efficiently.
Data Source
AI summary
An approach to optimizing heating, ventilation, and air conditioning (HVAC) temperature setpoints in multi-zone buildings uses a graph-based reinforcement learning framework enhanced with transfer learning. A thermal interaction graph is constructed from spatially distributed building zones, where nodes represent individual rooms or spaces and edges represent thermal or physical relationships. Environmental and operational data, including occupancy, temperature, and weather conditions, are encoded into the graph and processed by a graph neural network to generate a dynamic thermal state representation. A reinforcement learning agent is trained using this representation to learn control policies that adjust HVAC setpoints in real time to minimize energy consumption while maintaining occupant comfort. Transfer learning techniques are employed to adapt pretrained models from one building or zone configuration to another, significantly reducing training time and improving scalability across diverse building types. The system integrates with building management systems (BMS) via programmable interfaces, enabling real-time setpoint optimization.