HVAC Rule Mining for Occupancy-Aware Energy Control
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Solution Overview
Problem
HVAC systems in commercial and residential structures face inefficiencies in energy consumption due to inaccurate temperature forecasting, leading to unnecessary energy expenditure and occupant discomfort, especially during unoccupied periods.
Innovation Solution
An energy control system that collects and clusters sensor data from HVAC systems and structures, using association rule mining to generate rules that optimize operating characteristics, such as adjusting temperature settings and energy usage based on patterns identified in energy consumption patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If the HVAC system adjusts temperature quickly to respond to outdoor temperature changes, then occupant comfort is improved, but electrical energy consumption increases
Solution Approach 1:
The system performs preliminary action by forecasting future outdoor temperatures and pre-adjusting the indoor temperature before occupants arrive or before extreme conditions occur. The forecasting module predicts temperature trends, and the control module proactively modifies HVAC operation in advance, avoiding the need for rapid reactive adjustments that consume excessive energy.
Solution Approach 2:
The system implements dynamic adjustment by continuously adapting HVAC operation based on real-time sensor data, forecasted conditions, and learned occupancy patterns. Rather than fixed schedules or simple thermostatic control, the system dynamically optimizes temperature setpoints and equipment operation to balance comfort and energy consumption under varying conditions.
2Temperature
If the HVAC system conditions the entire structure, then occupant comfort is maintained, but electrical energy is wasted during unoccupied periods
Solution Approach 1:
The system applies local quality by differentiating temperature control across different zones or areas of the structure based on actual occupancy. The sensor module detects occupancy status in various locations, and the control module adjusts HVAC operation locally - maintaining comfortable temperatures in occupied zones while reducing or suspending conditioning in unoccupied zones, thereby eliminating energy waste.
Solution Approach 2:
The system enables self-service through automated occupancy detection and response. The sensor module continuously monitors occupancy status, and the control module automatically adjusts HVAC operation without manual intervention. The system serves itself by learning occupancy patterns and autonomously making optimal control decisions, reducing energy consumption during unoccupied periods while maintaining comfort when needed.
3Use of energy by moving object
If accurate temperature forecasting is implemented, then energy consumption is optimized, but system complexity increases
Solution Approach 1:
The system replaces complex mechanical forecasting mechanisms with data-driven computational methods. Rather than using sophisticated physical models or expert systems that would increase complexity, the invention uses sensor data collection, pattern recognition, and rule-based control algorithms to achieve accurate temperature forecasting and optimization with relatively simple implemented components.
Data Source
AI summary
A method of operating a heating ventilation and air conditioning (HVAC) system of a structure, includes collecting first sensor data corresponding to a parameter of the HVAC system, collecting second sensor data that is different than the first sensor data, and generating clustered data by clustering the first sensor data and the second sensor data into a plurality of data clusters with a controller. The method also includes forming a transactional dataset based on at least the first sensor data, the second sensor data, and the clustered data with the controller, performing association rule mining (ARM) on the transactional dataset to generate a plurality of rules for each data cluster of the plurality of data clusters with the controller, and changing an operating characteristic of the HVAC system based on the plurality of rules with the controller to optimize the parameter.