Building Automation Predictive Control Using Time-Based Simulation
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Solution Overview
Problem
Building automation systems lack a unified operational model to effectively manage energy interrelationships between various building components, relying on model-free control loops that struggle with sophisticated, tightly-coupled systems and adaptive tuning.
Innovation Solution
A closed-loop, heuristic, model-based predictive control algorithm is implemented, using a simulation engine to predict future behavior of building systems, optimizing resource use by evaluating physical models and selecting control regimes based on a cost function, with user input and external factors considered.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If model-free control loops are used to manage building systems, then implementation is simpler, but the system cannot effectively manage sophisticated, tightly-coupled systems or adaptively tune complex models
Solution Approach 1:
The system changes parameters by transitioning from model-free control to model-based predictive control, where system behavior is described through mathematical models with adjustable parameters. These parameters are continuously tuned based on actual system performance and sensor data, enabling adaptive optimization of HVAC, lighting, and other building systems while maintaining manageability through structured model parameter adjustment.
Solution Approach 2:
The control system performs self-service through automatic model parameter tuning and optimization. The system uses sensor data to continuously refine its predictive models and control strategies without requiring manual intervention, enabling sophisticated building systems to self-optimize their performance, energy consumption, and operational parameters autonomously.
2Adaptability or versatility
If a unified operational model is implemented to manage energy interrelationships, then intelligent management of building components is improved, but system complexity increases
Solution Approach 1:
The unified operational model is segmented into modular components, each representing a specific building system or function (HVAC, lighting, irrigation, etc.). These modular models can be independently developed, validated, and tuned, then integrated into the unified framework. This segmentation reduces overall system complexity while enabling intelligent management of energy interrelationships between different building components.
Solution Approach 2:
The patent introduces a simulation engine and cost function as intermediary layers between the physical building systems and the control decisions. These intermediaries translate complex physical relationships into manageable computational models, allowing intelligent management of energy interrelationships without directly managing the full complexity of the underlying physical systems.
3Productivity
If simulation engine is used to predict future behavior and optimize control regimes, then resource management efficiency is improved, but computational requirements and processing time increase
Solution Approach 1:
The simulation engine performs preliminary actions by predicting future system behavior and optimizing control regimes in advance before actual operations occur. By simulating and determining optimal control strategies ahead of time based on forecasted conditions, the system prepares optimization plans that can be executed without real-time computational delays, improving resource management efficiency while reducing processing time during actual operation.
Data Source
AI summary
Apparatuses, systems, methods, and computer program products are presented for a building-automation system controller. A building-automation system controller manages and/or controls energy, thermal, and/or functional systems and subsystems thereof utilizing a sensor, a physical model, a simulation engine, one or more predictive control loops, an optimal cost function, and an error band. A control loop is designed to utilize a simulation engine to predict a simulated predicted sensor value of a controlled system under a simulated control regime. A simulated control regime having an optimal cost function is selected for a controlled system until the controlled system diverges from the simulated predicted sensor value beyond an error band indicating uncertainty in a predicted future behavior so that a control loop is formed utilizing a simulation engine to predict a different future behavior in response to the controlled system diverging from the simulated predicted sensor value.