Predictive HVAC Zone Control for Comfort and Energy Optimization
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Solution Overview
Problem
Existing building HVAC control systems lack granular thermal zone-level comfort control while optimizing overall energy use, failing to meet the complex thermal comfort requirements of modern buildings and networks.
Innovation Solution
A predictive building control method and system that uses a processor to determine set points for HVAC systems based on historical data and forecast ambient temperatures, optimizing energy use by maintaining desired temperature ranges in individual thermal zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional BEMS uses predefined set points executed by HVAC system, then building energy management is simplified, but thermal comfort requirements in every thermal zone cannot be met while minimizing overall energy use
Solution Approach 1:
The patent segments the building into multiple thermal zones, each with its own comfort requirements and HVAC controls. The system divides the building envelope into discrete thermal zones that can be independently monitored and controlled, allowing granular temperature management in each zone while optimizing overall building energy consumption.
Solution Approach 2:
The patent implements dynamic set point adjustment based on real-time weather forecasts and actual building conditions. Instead of static predefined set points, the system continuously adapts HVAC control parameters responding to changing ambient temperatures, occupancy patterns, and energy prices, enabling both ease of operation and adaptability to thermal comfort requirements.
2Adaptability or versatility
If HVAC systems are controlled to meet granular thermal comfort requirements in every thermal zone, then thermal comfort is improved, but overall building energy use and costs increase
Solution Approach 1:
The patent uses weather forecast data to perform preliminary cooling or heating actions before peak temperature periods occur. By pre-conditioning thermal zones based on predicted weather patterns, the system reduces the need for intensive HVAC operation during extreme conditions, thereby maintaining comfort while reducing overall energy consumption.
Solution Approach 2:
The patent dynamically changes HVAC control parameters including set point temperatures, supply air temperatures, and equipment runtime based on real-time conditions and predictions. The system adjusts multiple parameters simultaneously to optimize the balance between thermal comfort and energy consumption, allowing granular zone control without proportionally increasing total energy use.
3Use of energy by stationary object
If predictive models use historical data and weather forecasts to determine optimal set points, then energy optimization is improved, but system complexity increases
Solution Approach 1:
The patent introduces a predictive control system that acts as an intermediary between weather forecast data and HVAC control. This intermediary layer processes weather predictions, historical building response data, and current conditions to generate optimized set points, simplifying the overall control architecture while achieving energy optimization that would be difficult with direct control methods.
Solution Approach 2:
The patent uses historical building response data as a copy of past performance patterns to predict future behavior. By training predictive models on historical data representing typical building responses to various weather conditions, the system creates a digital twin or model that can predict optimal control actions without requiring complex real-time simulations, thereby reducing system complexity while maintaining optimization capability.
Data Source
AI summary
A method for controlling temperature in a thermal zone within a building, comprising: using a processor, receiving a desired temperature range for the thermal zone; determining a forecast ambient temperature value for an external surface of the building proximate the thermal zone; using a predictive model for the building, determining set points for a heating, ventilating, and air conditioning (“HVAC”) system associated with the thermal zone that minimize energy use by the building; the desired temperature range and the forecast ambient temperature value being inputs to the predictive model; the predictive model being trained using respective historical measured value data for at least one of the inputs; and, controlling the HVAC system with the set points to maintain an actual temperature value of the thermal zone within the desired temperature range for the thermal zone.