HVAC workload and cost logic
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Solution Overview
Problem
Current HVAC systems are inefficient due to centralized temperature control, leading to wasted energy as different areas of a building experience varying heating and cooling needs, with existing systems relying solely on user input and failing to adapt to real-time indoor and outdoor conditions.
Innovation Solution
Implementing a method that detects a thermostat set to a target temperature, calculates the estimated runtime and cost of HVAC cycles based on current indoor and outdoor conditions using a correlation database, and suggests adjusted temperature settings to optimize energy use and detect potential faults in the HVAC system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized temperature control is used in HVAC systems, then system simplicity is maintained, but energy efficiency deteriorates due to wasted energy from not adapting to varying needs in different areas
Solution Approach 1:
The patent segments the building into multiple zones with independent temperature control capabilities. Each zone can be controlled individually based on its specific heating and cooling needs, allowing the system to adapt to varying conditions in different areas rather than applying centralized control uniformly across the entire building.
Solution Approach 2:
The patent implements local quality by allowing different zones within the building to have different temperature settings and control strategies. Each zone's HVAC equipment operates independently based on local conditions, enabling energy-efficient control tailored to specific area requirements rather than uniform centralized control.
2Ease of operation
If existing HVAC systems rely solely on user input, then system operation is simple, but adaptability deteriorates due to failure to respond to real-time indoor and outdoor conditions
Solution Approach 1:
The patent incorporates feedback mechanisms where sensors continuously monitor indoor and outdoor conditions such as temperature, humidity, and occupancy. This real-time data feeds back to the control system, which automatically adjusts HVAC operation to respond to changing conditions without requiring manual user input, thus maintaining ease of operation while dramatically improving adaptability.
Solution Approach 2:
The patent enables the HVAC system to serve itself by implementing automated control algorithms that use sensor data to make real-time decisions about heating, cooling, and equipment operation. The system self-adjusts based on detected conditions without human intervention, combining operational simplicity with high adaptability to real-time conditions.
3Device complexity
If manual temperature adjustment is used, then system complexity is low, but productivity deteriorates due to inability to optimize energy use and HVAC cycle runtime
Solution Approach 1:
The patent applies preliminary action by pre-cooling or pre-heating spaces before peak occupancy periods or extreme outdoor conditions. The system uses weather forecasts and occupancy schedules to anticipate future conditions and adjusts HVAC operation in advance, optimizing energy use and reducing peak load requirements without adding significant system complexity.
Solution Approach 2:
The patent implements dynamic control where HVAC system parameters such as temperature setpoints, equipment runtime, and operational schedules are continuously adjusted based on real-time conditions. This dynamic optimization maximizes energy efficiency and productivity by adapting system operation to changing indoor and outdoor environments rather than using static manual settings.
Data Source
AI summary
A method for HVAC workload and cost logic is described. In one embodiment, the method includes detecting a thermostat of an HVAC system being set to a target temperature and upon detecting the thermostat being set to the target temperature, detecting a current indoor condition and a current outdoor condition. In some embodiments, the method includes calculating an estimated runtime of an HVAC heating or cooling cycle for the target temperature. The estimated runtime is based on the target temperature, the current indoor and outdoor conditions, and on a result of querying a correlation database. The correlation database includes data points for a plurality of previous HVAC heating and cooling cycles.