Enclosure Thermal Modeling for Forecast-Driven HVAC Control
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Solution Overview
Problem
HVAC control systems face challenges in efficiently conditioning buildings due to the lack of accurate and dynamic modeling of enclosure thermal behavior, leading to suboptimal energy efficiency and increased costs, as conventional models are static and do not account for real-time weather and occupancy data.
Innovation Solution
The system updates the enclosure model using weather forecast data, historical data, occupancy data, and real-time sensor readings to predict future thermal conditions, allowing for dynamic control of HVAC systems, including induced changes to refine the model, such as heating or cooling, especially when the enclosure is unoccupied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static structural model is used to specify HVAC system behavior, then the model is simple to create and implement, but it does not account for real-time weather and occupancy data, leading to suboptimal energy efficiency
Solution Approach 1:
The patent transforms the static structural model into a dynamic model that continuously updates using real-time weather forecast data, historical data, and occupancy information. This dynamic updating process allows the HVAC system to adapt to changing conditions, optimizing energy efficiency while maintaining manageable complexity through automated algorithms.
Solution Approach 2:
The system uses weather forecast data to predict future thermal conditions and proactively adjusts HVAC operations in advance. By performing preliminary actions based on predicted weather patterns and occupancy schedules, the system optimizes energy efficiency before actual conditions occur, rather than reacting in real-time.
2Reliability
If the enclosure model is updated continuously using weather forecast data and sensor readings, then energy efficiency and thermal comfort are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent implements a feedback mechanism where real-time sensor readings from the enclosure are continuously compared against model predictions. The discrepancies feed back into the model updating process, refining the enclosure model's accuracy over time. This closed-loop feedback system maintains high model reliability while managing complexity through automated adjustment algorithms.
Solution Approach 2:
The system performs self-updating of the enclosure model using automated processing of weather forecast data, historical data, and sensor readings. The model refines itself without requiring manual intervention, maintaining high accuracy while reducing the operational complexity burden on users.
3Productivity
If weather forecast data including predictions more than 24 hours in the future is used, then long-term energy optimization is achieved, but the data processing and model updating complexity increases
Solution Approach 1:
The system processes and stores weather forecast data extending more than 24 hours into the future, enabling long-term preliminary actions in HVAC operations. By having access to extended forecast data, the system can plan energy optimization strategies in advance, maintaining high productivity while managing data processing complexity through efficient storage and selective processing of forecast information.
Data Source
AI summary
Systems and methods for modeling the behavior of an enclosure for use by a control system of an HVAC system are described. A model for the enclosure that describes the behavior of the enclosure for use by the control system is updated based on a weather forecast data. The weather forecast data can include predictions more than 24 hours in the future, and can include predictions such as temperature, humidity and/or dew point, solar output, precipitation. The model for the enclosure can also be updated based on additional information and data such as historical weather data such as temperature, humidity, wind, solar output and precipitation, occupancy data, such as predicted and/or detected occupancy data, calendar data, and data from the one or more weather condition sensors that sense current parameters such as temperature, humidity, wind, precipitation, and/or solar output. The model for the enclosure can be updated based also on an enclosure model stored in a database, and/or on enclosure information from a user. The model can be updated based on active testing of the enclosure which can be performed automatically or in response to user input. The testing can include heating and/or cooling the enclosure at times when the enclosure is not likely to be occupied.