Systems and methods of optimizing HVAC control in a building or network of buildings
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Solution Overview
Problem
Existing HVAC systems in buildings operate inefficiently due to being designed and managed as isolated components, lack integration with occupancy and environmental data, and fail to adapt to daily temperature fluctuations, leading to high energy consumption and comfort issues.
Innovation Solution
A system utilizing an edge computing device and remote server to collect data from environmental sensors and HVAC components, apply predictive algorithms for dynamic thermal equilibrium, and adjust HVAC operations based on occupancy, weather forecasts, and building thermodynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing thermostats are replaced with thermostats with occupancy sensors to link temperature control with room occupancy, then HVAC energy efficiency is improved, but the cost and effort of replacement becomes significant
Solution Approach 1:
The patent introduces an intermediary processing system that receives data from existing thermostats and environmental sensors, then generates optimized HVAC control commands. This mediator enables advanced occupancy-based control without replacing the physical thermostat hardware, thus improving energy efficiency while avoiding significant replacement costs.
Solution Approach 2:
The control system is designed to work with existing thermostat hardware while adding multiple functions through software processing, including occupancy detection, predictive algorithms, and environmental variable integration. This multi-functionality approach allows the system to perform advanced control tasks without requiring specialized hardware replacements.
2Quantity of substance
If BMS keeps only trend logs for limited time to avoid storing large datasets, then storage infrastructure requirements are reduced, but behavior-learning analysis capability is lost
Solution Approach 1:
The patent extracts only the essential features and patterns from raw HVAC data using processing algorithms, storing compressed representations rather than complete historical datasets. This extraction approach retains the critical information needed for behavior-learning analysis while significantly reducing storage requirements.
Solution Approach 2:
The system transforms raw operational data into processed parameters and patterns that capture thermal energy behaviors. By changing the data representation from raw values to extracted features, the system maintains analytical capability while reducing data volume for long-term storage.
3Ease of operation
If HVAC systems operate with fixed control sequences designed for typical days, then system operation is simplified, but responsiveness to actual daily conditions and fluctuations is reduced
Solution Approach 1:
The patent implements dynamic control sequences that adapt to actual daily conditions by processing real-time environmental data and occupancy information. The system automatically adjusts HVAC operation based on measured conditions rather than following fixed predetermined sequences, enabling responsiveness to fluctuations while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The control system incorporates continuous feedback from environmental sensors and thermostat data to adjust HVAC operation in real-time. This feedback mechanism allows the system to respond to actual conditions while automatically managing the complexity of adaptive control, maintaining ease of operation through automated responses.
4Productivity
If HVAC equipment is designed to operate efficiently in isolation as independent units, then individual equipment performance is optimized, but overall building HVAC ecosystem efficiency is reduced
Solution Approach 1:
The patent merges control of multiple independent HVAC equipment units into a coordinated ecosystem managed by a central processing system. By combining individual equipment operations under unified control that considers building-wide conditions, the system achieves overall efficiency improvements while maintaining individual equipment functionality.
Data Source
AI summary
A system and method for managing HVAC components of a building are disclosed. An example computer-implemented method includes: collecting and analyzing sensor data from sensors in the building; determining, based on the analytics of the sensor data, a plurality of macro parameter values for heating, cooling and ventilation for the building; generating, based on the plurality of macro parameter values, micro parameter values comprising a plurality of forecast values for at least one command point of the HVAC components of the building; and instructing the HVAC components on how to operate using the micro parameter values. An artificial intelligence engine may be implemented to predict operating values for the HVAC components based on the macro or micro parameter values.