HVAC Workload and Cost Logic Using Historical Runtime Correlation
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Solution Overview
Problem
Traditional 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, resulting in inadequate comfort and increased costs.
Innovation Solution
Implementing HVAC workload and cost logic that detects current indoor and outdoor conditions, calculates estimated runtime and cost of HVAC cycles, and suggests optimal temperature settings based on historical data correlations, allowing for real-time adjustments and notifications to optimize energy use.
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 varying heating and cooling needs in different areas
Solution Approach 1:
The patent segments the building into multiple thermal zones with independent temperature control. Each zone has its own thermostat and HVAC control, allowing differentiated heating and cooling based on local conditions. This segmentation resolves the contradiction by enabling energy efficiency through localized control while maintaining overall system manageability.
Solution Approach 2:
The patent implements local quality by allowing different zones to have different temperature settings and HVAC operational characteristics based on their specific requirements. Each zone's HVAC system operates independently according to local thermal conditions, occupancy patterns, and user preferences, thereby reducing wasted energy from uniform centralized control.
2Loss of energy
If multiple independent zone controls are implemented, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple zone control systems into a unified HVAC management platform that coordinates temperature control across zones. The system integrates zone thermostats, building management software, and HVAC equipment control into a cohesive architecture, reducing operational complexity while maintaining the energy efficiency benefits of distributed control.
Solution Approach 2:
The patent creates a universal HVAC control system that can manage multiple zones with different requirements through a single integrated platform. The system provides multi-functional capabilities including temperature regulation, energy optimization, fault detection, and user interface management across all zones, thereby simplifying operation despite the complexity of distributed control.
3Ease of operation
If real-time HVAC monitoring and adjustment is implemented, then comfort is improved, but system complexity and cost increase
Solution Approach 1:
The patent implements self-service through automated HVAC control algorithms that monitor zone conditions and automatically adjust temperature settings without user intervention. The system uses sensors, thermostats, and control software to detect thermal conditions and autonomously regulate HVAC equipment, improving comfort while minimizing operational complexity for users.
Solution Approach 2:
The patent incorporates feedback mechanisms where zone thermostats continuously monitor temperature conditions and communicate with the HVAC control system. The system uses this feedback to dynamically adjust heating and cooling operations, ensuring comfort maintenance while optimizing energy consumption. The feedback loop enables automatic adaptation to changing conditions without increasing user burden.
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.