HVAC Control Kit Using Forecast-Based Simulation to Reduce Energy Loss
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing building management systems (BMS) and building automation systems (BAS) face challenges in efficiently controlling HVAC systems due to their reliance on fixed control sequences, lack of detailed data history, and the need for costly replacements of thermostats with occupancy sensors.
Innovation Solution
A computer-implemented system that dynamically controls HVAC components by receiving user objectives, forecasts, and current states of a building, performing HVAC control simulations, selecting optimal control modules, and deploying them to control HVAC components, thereby optimizing energy usage and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If HVAC systems are controlled by traditional BMS with fixed control sequences, then the system operation is simple and reliable, but energy efficiency deteriorates due to inability to adapt to dynamic conditions
Solution Approach 1:
The patent implements dynamic control sequences that automatically adjust HVAC operation based on real-time weather forecasts and actual building conditions. The system transitions from static, pre-programmed control to dynamic, adaptive control that responds to changing environmental conditions, occupancy patterns, and energy prices, thereby reducing energy consumption without requiring complex manual intervention.
Solution Approach 2:
The system continuously monitors actual building conditions (temperature, humidity, occupancy) and compares them against forecasted conditions and control objectives. This feedback loop enables the system to learn from past performance and optimize future control decisions, improving energy efficiency while maintaining system reliability through data-driven adjustments rather than complex mechanical modifications.
2Measurement precision
If detailed historical data is stored for behavior-learning analysis, then predictive control accuracy improves, but data storage requirements and infrastructure complexity increase
Solution Approach 1:
The patent extracts only the most critical data elements needed for predictive control (weather forecasts, occupancy patterns, energy consumption metrics) while filtering out redundant information. By focusing on essential data points rather than storing all possible building operational data, the system achieves high predictive accuracy without requiring massive storage infrastructure, thus resolving the contradiction between precision and storage volume.
Solution Approach 2:
The system transforms raw building operational data into meaningful parameters and patterns that capture thermal energy behavior. By changing the representation of data from raw measurements to processed behavioral parameters, the system achieves high predictive control accuracy with compact data storage, as the transformed parameters contain more information density per unit of storage.
3Productivity
If occupancy sensors are added to link temperature control with room occupancy, then HVAC efficiency improves, but installation cost and device complexity increase
Solution Approach 1:
The patent makes existing thermostats perform multiple functions by integrating forecast-based control logic and connectivity to central building management systems. Rather than requiring separate occupancy sensors in each room, the system uses the existing thermostat infrastructure to deliver occupancy-aware control through centralized data processing and control sequence adjustment, thereby improving efficiency without increasing installation costs or device complexity at the point of use.
4Loss of energy
If HVAC equipment operates at fixed speeds with simple on/off control, then equipment reliability is maintained, but energy efficiency deteriorates due to inability to optimize for actual conditions
Solution Approach 1:
The system uses weather forecasts and predictive models to determine optimal HVAC operation in advance. By pre-calculating control sequences based on forecasted conditions and building thermal characteristics, the system can optimize energy efficiency without requiring real-time equipment speed modulation, thus maintaining equipment reliability while reducing energy consumption through proactive rather than reactive control.
Data Source
AI summary
A system and method for controlling HVAC components of a building are disclosed, the method including: receiving user objective indicators, each indicating a corresponding user objective for the HVAC components; receiving a plurality of forecasts, each predicting a dynamic state of the building or a usage parameter of one of the HVAC components; receiving a plurality of current states of the building; maintaining a plurality of control modules; and upon detecting a change in at least one of the forecasts or at least one of the user objective indicators: performing a plurality of HVAC control simulations, each simulating the performance of a corresponding subset of the control modules; selecting a subset of the control modules based on results of the simulations and the one or more user objective indicators; and deploying the selected subset of control modules to control the HVAC components.